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Caluga - The Java Blog

this blog will cover topics all around Java, opensource, Mongodb and alike. Especially Morphium - THE POJO Mapper for mongodb

found results: 71

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category: Java --> programming --> Computer

new release of Morphium V3.1.0

2016-11-02 - Tags:

sorry, no english version available

category: Computer

First Beta release of Morphium 3.0

2016-04-07 - Tags: java-2 mongodb morphium-2

sorry, no english version available

category: Java --> programming --> Computer

Logging in Java - example in Morphium

2016-03-02 - Tags:

sorry, no english version available

category: Java --> programming --> Computer

Update on Morphium 3.0

2016-02-25 - Tags: english java-2 morphium-2

sorry, no english version available

category: Computer

It happened - this site was hacked... partly.

2016-02-15 - Tags:

sorry, no english version available

category: Java --> programming --> Computer

Morphium V3.0ALPHA

2016-01-18 - Tags:

sorry, no english version available

category: Computer

Java8 and Vector - yes, you can use it again!

2015-11-23 - Tags:

I collegue of mine came to me today and mentionend, that the use of ArrayList would cause problems in multithreadded environments - and he's right! At this very occasion it is discussing some internal cache of our application, where a lacking object here and there is not ab big deal. BUT: What we found out with his help is the following:

We were experimenting with lock and synchronized a bit, and found, that locks are way slower than using synchronized - in java 8 that is. There seems to be siginficant performance optimization in the synchonization in the VM itself. So, we wanted to compare the access to a list in a multithreadded environment and measure the timings. Here is the method, we used:

 private void testIt(final List lst) {
    long start = System.currentTimeMillis();
    int threads = 300;
    threadCount = 0;
    for (int i = 0; i < threads; i++) {
        final int j = i;
        new Thread() {
            public void run() {
                for (int k = 0; k < 1000; k++) {
//                        synchronized (lst) {
                    try {
                        lst.add("hello " + j + " - " + k);
                    } catch (Exception e) {
//                        }

    while (threadCount < threads) {
    long dur = System.currentTimeMillis() - start;
    System.out.println("write took : " + dur);
    System.out.println("Counting   : " + lst.size() + " missing: " + ((threads * 1000) - lst.size()));
    threadCount = 0;
    start = System.currentTimeMillis();
    for (int i = 0; i < threads; i++) {
        final int j = i;
        new Thread() {
            public void run() {
                for (int k = 0; k < 1000; k++) {
//                        synchronized (lst) {
                    try {
                        if (j * 1000 + k < lst.size())
                            lst.get(j * 1000 + k);
                    } catch (Exception e) {
//                        }

    while (threadCount < threads) {
    dur = System.currentTimeMillis() - start;
    System.out.println("read took : " + dur);

The code does not do much: creates 300 Threads, each of those storing data into a shared List of certain type. And after that, we create 300 threads reading those values (if they are there, that is - when using non-threadsafe datastructures, you will end up with data missing!).

Here is the result:

Testing with ArraList
write took : 83
Counting   : 255210 missing: 44790
read took : 22

Testing with Vector
write took : 64
Counting   : 300000 missing: 0
read took : 89

Testing with LinkedList
write took : 38
Counting   : 249998 missing: 50002
read took : 13367

Everybody knows, it is not a good idea to use Vector - itÔÇÖs old and sluggish, slow and not useful. Do your own synchronization... This has been true obvously till JDK 1.7 - We ran the same test with JDK1.7 and Vector was at least 3 as slow as ArrayList or Linkedlist (only faster in reading).

We were shocked to see, that Vector ist actually faster than ArrayList! Significantly! And Thread-Safe! And it is even faster than using the same code with a synchronized block when accessing the list (see the commented out synchronized statements in the code above):

Testing with ArraList (synchronized block)
write took : 191
Counting   : 300000 missing: 0
read took : 80

Testing with Vector
write took : 68
Counting   : 300000 missing: 0
read took : 79

Testing with LinkedList (synchronized block)
write took : 178
Counting   : 300000 missing: 0

Of course, this is not a total in depth analysis as we actually donÔÇÖt know for sure, what is causing this performance increase. But it really is reassuring - love to see, that Vector got some love a gain ;-) So - in an Java8 environment, you could actually use Vector without having to think about performance issues...

Update: I just compared the creation times (Default constructor) of the different types also, these are the timings:

Duration vector    : 31ms
Duration ArrayList : 2ms
Duration LinkedList: 3ms

So, what remains is: use Vector, if you do not create too many instances of it ­čśë

2nd Update: I just want to make things about this test a bit more clear. People tend to tell me that "this is no proper test, no Warmup phase, no proper Threadding... yadda yadda".

you might be surprised, YES I KNOW!

Instead of discussing the Idea, they discuss the toolset... facepalm my fault. Thought, this was clear from the beginning. Sorry for that.

This piece does not try to be the proof of anything. It is just showing, that there is some significant performance increase on Java 8 vs java 7 when it comes to Vector. Also, as already mentioned above, this code was not created like this, it is just a "byproduct".

The rest of this was to put in in perspective. Agreed, this was not very clear. It shows, that when your data structure is not synchornized, you might end up with data being lost. The test quantifies this loss with numbers. Which is also interesting - but for a different topic.

The results of this piece of code are reproduceable. Which means, that the numbers might differ, but comparing everything, the numbers are quite in the same area. Again, this is not a proper micro benchmark! This is better solved with something else, I agree.

So the goal was never to prove something, it is only a hint, that even Vector might be worth trying. It is still around, right? not marked deprecated, and not used as it is "slow". This is maybe not true to the extend it used to be.

But: to make things clear. As it seems in further Tests (those were done with the JMH-Testing framework), that often the Collections.syncrhonizedList(new ArrayList<>()) returns a better performing version than Vector.

But again: this whole thing here just wants to show that the huge performance loss you got when using Vector in JDK1.7 and before is now a bit smaller... and in some cases even gone!

category: Computer

Stephans Blog wieder online...

2015-06-12 - Tags: allgemein blog

originally posted on:

no english version available yet

Das war stressig. Zum Umzug kam noch hinzu, dass mein Server die Gr├Ątsche gemacht hat. Ich musste neu installieren. Was ja ÔÇô dank Backups ÔÇô eigentlich kein allzu gro├čer Aufwand w├Ąre, h├Ątte ich nicht vergessen, ein Backup von der Datenbank zu machenÔÇŽ Deswegen jetzt der neue Start des alten Blogs ;-)

category: Computer

Feature Release Morphium 2.2.16

2015-01-22 - Tags: java-2 morphium-2

sorry, no english version available

category: MongoDB --> programming --> Computer

Additional Feature Release V2.2.10 morphium

2014-09-29 - Tags:

sorry, no english version available

category: MongoDB --> programming --> Computer

Feature Release of Morphium V2.2.9

2014-09-28 - Tags:

sorry, no english version available

category: Computer --> programming --> MongoDB --> morphium

Morphium Documentation

2014-09-05 - Tags: morphium java mongo

want help translating / documenting / coding? Conctact us on github or via slack

Morphium Documentation

What is Morphium

Morphium started as a feature rich access layer and POJO mapper for MongoDB in java. It was built with speed and flexibility in mind. So it supported cluster aware caching out of the box, lazy loading references and much more. The POJO Mapping is the core of Morphium, all other features were built around that. It makes accessing MongoDB easy, supports all great features of MongoDB and adds some more.

But with time, the MongoDB based messaging became one of the most popular features in Morphium. It is fast, reliable, customisable and stable.

About this document

This document is a documentation for Morphium in the current (4.2) version. It would be best, if you had a basic understanding of MongoDB and a good knowledge on Morphium. If you want to know about MongoDB's features, that Morphium implements here, have a look at the official MongoDB pages and the documentation there.

This documentation covers all features Morphium has to offer.

Later in this document there are chapters about the POJO mapping, querying data and using the aggregation framework. Also a chapter about the InMemory driver, which is quite useful for testing. But let's start with the messaging subsystem first.

Using Morphium as a messaging system

Morphium itself is simple to use, easy to customise to your needs and was built for high performance and scalability. The messaging system is no different. It relies on the watch functionality, that MongoDB offers since V3.6 (you can also use messaging with older versions of MongoDB, but it will result in polling for new messages). With that feature, the messages are pushed to all listeners. This makes it a very efficient messaging system based on MongoDB.

why Morphium messaging

There is a ton of messaging solutions out there. All of them have their advantages and offer lots of features. But only few of them offer the things that Morphium has:

  • the message queue can easily be inspected and you can use mongo search queries to find the messages you are looking for1
  • the message queue can be altered (update single messages with ease, delete messages or just add new messages)
  • Possibility to broadcast messages, that are only processed by one client max (Exclusive Messages). With V4.2 of Morphium this also works with a group of recipients.
  • Messaging is multithreaded and thread safe
  • pausing and unpausing of message processing without data loss
  • Morphium messaging picks up all pending messages on startup - no data loss.
  • no need to install additional servers or provide separate infrastructure. Just use your MongoDB you likely already have in place.

There are people out there using Morphium and its messaging for production grade development. For example uses Morphium messaging to power a microservice architecture with an enterprise message bus.

Quick start Messaging

Morphium m=new Morphium();
Messaging messaging=new Messaging(m);

messaging.addMessageListener((messaging, msg) -> {"Got message!");
return null;  //not sending an answer

This is a simple example of how to implement a message consumer. This consumer listens to all incoming messages, regardless of name.

Messages do have some fields, that you might want to use for your purpose. But you can create your own message type as well (see below). the Msg-Class defines those properties:

  • name the name of the Message - you can define listeners only listening to messages of a specific name using addListenerForMessageNamed. Similar to a topic in other messaging systems
  • msg: String message
  • value: well - a String value
  • mapValue: for more complex use cases where you need to send more information
  • additional: list value - used for more complex use cases
  • all messages do store some values for the processing algorithm, like processed_by, in_answer_to, timestamp etc.

So if you want to send a Message, that is also simple:

messaging.queueMessage(new Msg("name","A message","the value");

queueMessage is running asynchronously, which means, that the message is not directly stored. If you need more speed and shorter reaction time, you should use sendMessage instead (directly storing message to mongo).

Answering messages

Morphium is able to answer any message for you. Your listener implementation only needs to return an instance of the Msg-Class(fn). This will then be sent back to the sender as an answer.

When sending a message, you also may wait for the incoming answer. The Messaging class offers a method for that purpose:

//new messaging instance with polling frequency of 100ms, not multithreaded
//polling only used in case of non-Replicaset connections and in some
//cases like unpausing to find pending messages

Messaging sender = new Messaging(_Morphium_, 100, false);

gotMessage1 = false;
gotMessage2 = false;
gotMessage3 = false;
gotMessage4 = false;

Messaging m1 = new Messaging(_Morphium_, 100, false);
m1.addMessageListener((msg, m) -> {
gotMessage1 = true;
return new Msg(m.getName(), "got message", "value", 5000);


Msg answer = sender.sendAndAwaitFirstAnswer(new Msg("test", "Sender", "sent", 15000), 15000);
assert (answer != null);
assert (answer.getName().equals("test"));
assert (answer.getInAnswerTo() != null);
assert (answer.getRecipient() != null);
assert (answer.getMsg().equals("got message"));

As the whole communication is asynchronous, you will have to specify a timeout after wich the wait for answer will be aborted with an exception. And, there might be more than one answers to the same message, hence you will only get the first one.

in the above example, the timeout for the answer is set to 15s (and the TTL for messages also).

more advanced settings

Custom message classes

As mentioned above, you can define your own Message-Class to be send back and forth. This class just needs to extend the standard Msg-Class. When adding a listener to messaging, you have the option to also use generics to specify the Msg-Type you want to use.

Message priorities

Every message does have a priority field. That is used for giving queued messages precedence over others. The priority could be changed after a message is queued directly in MongoDB (or using Morphium).

But as the messaging is built on pushing of messages, when is the priority field used? Several cases:

  • when starting up messaging. When starting Messaging, the system does look for pending messages in the queue, highes prio is used first
  • when unpausing a messaging instance, it will look for any messages in the queue and will process them according to their priority.

Pausing / unpausing of messaging

In some cases it might be necessary to pause message processing for a time. That might be the case, if the message is triggering some long running task or so. If so, it would be good not to process any additional messages (at least of that type).

You can call messaging.pauseProcessingOfMessagesNamed to not process any more messages of a certain type.

Attention: if you have long running tasks triggered by messages, you should pause processing in the onMessage method and unpause it when finished.

Multithreading / Multimessage processing

When instantiating Messaging, you can specify two booleans:

  • multithreading: if true, every incoming message will be processed in an own thread (Executor - see MorphiumConfig below). That means, several messages can be processed in parallel
  • processMultiple: this setting is only important in case of startup or unpausing(fn). If true, messaging will lock all(fn) messages available for this listener and process them one by one (or in parallel if multithreading is enabled).

    These settings are influenced by other settings:
  • messagingWindowSize in MorphiumConfig or as constructor parameter / setter in Messaging: this defines how many messages are marked for processing at once. Those might be processed in parallel (depending whether processMultiple is true, and the executor configuration, how many threads can be run in parallel)
  • useChangeStream in Messaging. Usually messaging determines by the cluster status, whether or not to use the changestream or not. If in a cluster, use it, if not use polling. But if you explicitly want to use polling, you can set this value to false. The advantage here might be, that the messages are processed by priority with every poll. This might be useful depending on your usecase. If this is set to false (or you are connected to an single instance), the pause configuration option (aka polling frequency) in Messaging will determine how fast your messages can be consumed. Attention high polling frequency (a low pause value), will increase the load on MongoDB.
  • ThreadPoolMessagingCoreSize in MorphiumConfig: If you define messaging to be multithreaded it will spawn a new thread with each incoming message. this is the core size of the corresponding thread pool. If your messaging instance is not configured for multithreading, this setting is not used.
  • ThreadPoolMessagingMaxSize: max size of the thread pool. similar to above.
  • ThreadPoolMessagingKeepAliveTime: time of threads to live in ms

    some examples to clarify that:
  • your messaging instance is configured for multithreaded processing, multiple processing, having a windowSize of 100 and a ThreadPoolMessagingMaxSize of 10, then there will be 100 messages in queue marked for being processed by this specific messaging instance, but only 10 will be processed in parallel.
  • multithreaded processing is false, then the windowSize determines how many messages are marked for being processed, but are only processed one by one
  • multithreaded processing and multiple processing is false, then only one message is marked for being processed at a time. As soon as this processing is finished, the next message is being taken.
  • having multithreaded set to true and processMultiple set to false would result in running each message processing in one separate thread, but only one at a time. This is very similar to having multithreaded and process multiple both set to false.

Custom MessageQueue name

When creating a Messaging instance, you can set a collection name to use. This could be compared to having a separate message queue in the system. Messages sent to one queue are not being registered by another.

JMS Support

Morphium messaging also implements the standard JMS-API to a certain extend and can be used this way. Please keep in mind that JMS does not support most of the features, Morphium messaging offers, and that the JMS implementation does not cover 100% of the JMS API yet:

public void basicSendReceiveTest() throws Exception {
JMSConnectionFactory factory = new JMSConnectionFactory(morphium);
JMSContext ctx1 = factory.createContext();
JMSContext ctx2 = factory.createContext();

JMSProducer pr1 = ctx1.createProducer();
Topic dest = new JMSTopic("test1");

JMSConsumer con = ctx2.createConsumer(dest);
con.setMessageListener(message ->"Got Message!"));
pr1.send(dest, "A test");


public void synchronousSendRecieveTest() throws Exception {
JMSConnectionFactory factory = new JMSConnectionFactory(morphium);
JMSContext ctx1 = factory.createContext();
JMSContext ctx2 = factory.createContext();

JMSProducer pr1 = ctx1.createProducer();
Topic dest = new JMSTopic("test1");
JMSConsumer con = ctx2.createConsumer(dest);

final Map<String, Object> exchange = new ConcurrentHashMap<>();
Thread senderThread = new Thread(() -> {
JMSTextMessage message = new JMSTextMessage();
try {
} catch (JMSException e) {
pr1.send(dest, message);"Sent out message");
exchange.put("sent", true);
Thread receiverThread = new Thread(() -> {"Receiving...");
Message msg = con.receive();"Got incoming message");
exchange.put("received", true);
assert (exchange.get("sent") != null);
assert (exchange.get("received") != null);


The JMS Implementation uses the answering mechanism for acknowledging incoming messages. This makes JMS more or less half as fast as the direct usage of Morphium messaging.

Also, the implementation is very basic at the moment. A lot of methods lack implementation2. If you notice some missing functionality, just open an issue at github.

Because of the JMS Implementation being very basic at the moment, it should not be considered production ready!


Simple producer consumer setup:

Morphium m=new Morphium(config);
// create messaging instance with default settings, meaning
// no multithreading, windowSize of 100, processMultiple false
Messaging producer=new Messaging(m);

producer.queueMessage(new Msg("name","a message","a value"));

the receiver needs to connect to the same mongo and the same database:

Morphium m=new Morphium(config);
Messaging consumer=new Messaging(m);
consumer.start(); //needed for receiving messages

consumer.addMessageListener((messaging, msg) -> {
//Incoming message 
System.out.println("Got a message of name "+msg.getName());
return null; //no answer to send back

you can also register listeners only for specific messages:consumer.start(); //needed for receiving messages

consumer.addListenerForMessageNamed("name",(messaging, msg) -> {
//Incoming message, is always named "name"
System.out.println("Got value: "+msg.getValue());
Msg answer=new Msg(msg.getName(),"answer","the answerValue");
return answer; //no answer to send back

Attention: the producer will only be able to process incoming messages, if start() was called!

The message sent there was a broadcast message. All registered listeners will receive that message and will process it!

Direct messages

In order to send a message directly to a specific messaging instance, you need to get the unique ID of it. This id is add as sender to any message.

Msg m=new Msg("Name","Message","value");
//you could add more recipients if necessary

Background: This is used to send answers back to the sender. If you return a message instance in onMessage, this message will be sent directly back to the sender.

You can add as many recipients as needed, if no recipient is defined, the message by default is sent to all listeners.

Exclusive Broadcast messages

Broadcast messages are fine for informing all listeners about something. But for some more complex scenarios, you would need a way to queue a message, and have only one listener process it - no matter which one (load balancing?)

Morphium supports this kind of messages, it is called "exclusive broadcast". This way, you can easily scale up by just adding listener instances.

Sending a exclusive broadcast message is simple:

    Msg m=new Message("exclusive","The message","and value");

The listener only need to implement the standard onMessage-Method to get this message. Due to some sophisticated locking of messages, Morphium makes this message exclusive - which means, it is only processed once!

Since Morphium V4.2 it is also possible to send an exclusive message to certain recipients3.

The behaviour is the same: the message will only be processed by one of the specified recipients, whereas it will be processed by all recipients, if not exclusive.

InMemory Driver

One main purpose of the InMemoryDriver is to be able to do testing without having a MongoDB installed. The InMemoryDriver adds the opportunity to let all MongoDB-code run in Memory, with a couple of exceptions

  • unfortunately, the InMemoryDriver cannot do aggregations. It will throw an Exception, when trying Aggregations with this driver
  • the inMemoryDriver is also not capable to return cluster information, run mongodb commands
  • it does not support spacial indexes or queries

If you want to mock those things in testing, you need to:

  1. create a subclass of the inMemoryDriver
  2. override the corresponding method, for example aggregate() for aggregation and return the properly mocked data
  3. set the driver back to default in order to have it work
public void mockAggregation() throws Exception{
MorphiumDriver original=morphium.getDriver();
morphium.setDriver(new InMemoryDriver(){
public List<Map<String, Object>> aggregate(String db, String collection, List<Map<String, Object>> pipeline, boolean explain, boolean allowDiskUse, Collation collation, ReadPreference readPreference) throws MorphiumDriverException {
return Arrays.asList(Utils.getMap("MockedData",123.0d));

Aggregator<UncachedObject, Map> agg = morphium.createAggregator(UncachedObject.class, Map.class);        
assert(agg.aggregate().get(0).get("MockedData").equals(123.0d)); //checking mocked data

how to use the inMemory Driver

you just need to set the Driver properly in your Morphium configuration.

    MorphiumConfig cfg = new MorphiumConfig();
morphium = new Morphium(cfg);

Of course, the InMemDriver does not need hosts to connect to, but for compatibility reasons, you need to add at least one host (although it will be ignored).

You can also set the Driver in the settings, e.g. in properties:

morphium.driverClass = "de.caluga.morphium.driver.inmem.InMemoryDriver"

After that initialisation you can use this Morphium instance as always, except that it will "persist" data only in Memory.

Dumping InMemory data

As in memory storage is by definition not lasting, it might be a good idea to store your data onto disk for later use. The InMemoryDriver does support that:

public void driverDumpTest() throws Exception {
for (int i = 0; i < 100; i++) {
UncachedObject e = new UncachedObject();
e.setValue("value" + i);
e.setIntData(new int[]{i, i + 1, i + 2});
e.setBinaryData(new byte[]{1, 2, 3, 4, 5});;

ComplexObject o = new ComplexObject();
o.setEinText("A text " + i);
o.setEmbed(new EmbeddedObject("emb", "v1", System.currentTimeMillis()));


ByteArrayOutputStream bout = new ByteArrayOutputStream();

InMemoryDriver driver = (InMemoryDriver) morphium.getDriver();
driver.dump(morphium, morphium.getDriver().listDatabases().get(0), bout);"database dump is " + bout.size());

driver.restore(new ByteArrayInputStream(bout.toByteArray()));
assert (morphium.createQueryFor(UncachedObject.class).countAll() == 100);
assert (morphium.createQueryFor(ComplexObject.class).countAll() == 100);

for (ComplexObject co : morphium.createQueryFor(ComplexObject.class).asList()) {
assert (co.getEinText() != null);
assert (co.getRef() != null);

In this example, data is stored to a binary stream, which could also be stored to disk somewhere.

But you can also create a dump in JSON format, which makes it easier to edit and maybe to create from scratch:

public void jsonDumpTest() throws Exception {

MorphiumTypeMapper<ObjectId> mapper = new MorphiumTypeMapper<ObjectId>() {
public Object marshall(ObjectId o) {
Map<String, String> m = new HashMap<>();
m.put("value", o.toHexString());
m.put("class_name", o.getClass().getName());
return m;


public ObjectId unmarshall(Object d) {
return new ObjectId(((Map) d).get("value").toString());
morphium.getMapper().registerCustomMapperFor(ObjectId.class, mapper);
for (int i = 0; i < 10; i++) {
UncachedObject e = new UncachedObject();
e.setValue("value" + i);;
ExportContainer cnt = new ExportContainer();
cnt.created = System.currentTimeMillis(); = ((InMemoryDriver) morphium.getDriver()).getDatabase(morphium.getDriver().listDatabases().get(0));

Map<String, Object> s = morphium.getMapper().serialize(cnt);

ExportContainer ex = morphium.getMapper().deserialize(ExportContainer.class, Utils.toJsonString(s));
assert (ex != null);
((InMemoryDriver) morphium.getDriver()).setDatabase(morphium.getDriver().listDatabases().get(0),;

List<UncachedObject> result = morphium.createQueryFor(UncachedObject.class).asList();
assert (result.size() == 10);
assert (result.get(1).getCounter() == 1);

public static class ExportContainer {
public Long created;
public Map<String, List<Map<String, Object>>> data;

The JSON output of this little dump looks like this:

"_id" : 1599853076411,
"data" : {
"uncached_object_0" : [
"_id" : {
"class_name" : "org.bson.types.ObjectId",
"value" : "5f5bd214f8fd82e792ef3b51"
"counter" : 0,
"dval" : 0,
"value" : "value0"
"_id" : {
"class_name" : "org.bson.types.ObjectId",
"value" : "5f5bd214f8fd82e792ef3b53"
"counter" : 1,
"dval" : 0,
"value" : "value1"
"_id" : {
"class_name" : "org.bson.types.ObjectId",
"value" : "5f5bd214f8fd82e792ef3b55"
"counter" : 2,
"dval" : 0,
"value" : "value2"
"_id" : {
"class_name" : "org.bson.types.ObjectId",
"value" : "5f5bd214f8fd82e792ef3b57"
"counter" : 3,
"dval" : 0,
"value" : "value3"
"_id" : {
"class_name" : "org.bson.types.ObjectId",
"value" : "5f5bd214f8fd82e792ef3b59"
"counter" : 4,
"dval" : 0,
"value" : "value4"
"_id" : {
"class_name" : "org.bson.types.ObjectId",
"value" : "5f5bd214f8fd82e792ef3b5b"
"counter" : 5,
"dval" : 0,
"value" : "value5"
"_id" : {
"class_name" : "org.bson.types.ObjectId",
"value" : "5f5bd214f8fd82e792ef3b5d"
"counter" : 6,
"dval" : 0,
"value" : "value6"
"_id" : {
"class_name" : "org.bson.types.ObjectId",
"value" : "5f5bd214f8fd82e792ef3b5f"
"counter" : 7,
"dval" : 0,
"value" : "value7"
"_id" : {
"class_name" : "org.bson.types.ObjectId",
"value" : "5f5bd214f8fd82e792ef3b61"
"counter" : 8,
"dval" : 0,
"value" : "value8"
"_id" : {
"class_name" : "org.bson.types.ObjectId",
"value" : "5f5bd214f8fd82e792ef3b63"
"counter" : 9,
"dval" : 0,
"value" : "value9"

Morphium POJO Mapping

Ideas and design criteria

In the early days of MongoDB there were not many POJO mapping libraries available. One was called morphia. Unfortunately we had a lot of problems adapting this to our needs.

Hence we built Morphium and we named it similar to morphia to show where the initial idea came from.

Morphium is built with flexibility, thread safety, performance and cluster awareness in mind.

  • flexibility: it is possible to exchange most of the internal implementations of Morphium. You could have your own Driver class for connecting to MongoDB(fn) or have a custom implementation for the query processing.
  • thread safety: all aspects of Morphium were tested multithreaded so that it can be used in production
  • performance: one of the main goals of Morphium was to improve performance. The Object Mapping in use is a custom implementation that was built especially for Morphium, is very fast and to improve speed even further, caching is part of the core features of Morphium
  • cluster awareness: this is essential nowadays for high availability or just mere speed. _Morphium_s caches are all cluster aware which means you will not end up with dirty reads in a clustered environment when using Morphium(fn)
  • independent from mongoDB Driver: Morphium does not have a direct dependency on the mongoDB java driver, instead it considers it to be provided. This means, you can have a different version of the driver in use than the one Morphium was last tested with (you do not need the latest and grates, usually it is backward compatible). In addition to that, Morphium does not directly use MongoDB or BSON classes but offers its own implementation. For example the MorphiumId, wich is a drop in replacement for ObjectId.


Morphium is built to be very flexible and can be used in almost any environment. So the architecture needs to be flexible and sustainable at the same time. Hence it's possible to use your own implementation for the cache if you want to.

There are four major components of Morphium:

  1. the Morphium Instance: This is you main entry point for interaction with Mongo. Here you create Queries and you write data to mongo. All writes will then be forwarded to the configured Writer implementation, all reads are handled by the Query-Object
  2. Query-Object: you need a query object to do reads from mongo. This is usually created by using _Morphium_.createQueryFor(Class<T> cls). With a Query, you can easily get data from database or have some things changed (update) and alike.
  3. the Cache: For every request that should be sent to mongo, Morphium checks first, whether this collection is to be cached and if there is already a result being stored for the corresponding request.
  4. The Writers: there are 3 different types of writers in Morphium: The Default Writer (_Morphium_Writer) - writes directly to database, waiting for the response, the BufferedWriter (BufferedWriter) - does not write directly. All writes are stored in a buffer which is then processed as a bulk. The last type of writer ist the asynchronous writer (AsyncWriter) which is similar to the buffered one, but starts writing immediately - only asynchronous. Morphium decides which writer to use depending on the configuration and the annotations of the given Entities. But you can always use asynchronous calls just by adding aAsyncCallback implementation to your request.

Simple rule when using Morphium: You want to read -> Use the Query-Object. You want to write: Use the Morphium Object.

There are some additional features built upon this architecture:

  • messaging: Morphium has its own production grade messaging system. Its has a lot of features, that are unique for a messaging system.
  • cache synchronization: Synchronize caches in a clustered environment. Uses messaging.
  • custom mappers - you can tell Morphium how to map a certain type from and to MongoDB. For example there is a "custom" mapper implementation for mapping BigInteger instances to MongoDB.
  • every one of those implementations can be changed: it is possible to set the class name for the BufferedWriter to a custom built one (in MorphiumConfig). Also you could replace the object mapper with your own implementation by implementing the ObjectMapper interface and telling Morphium which class to use instead. In short, these things can be changed in Morphium / MorphiumConfig:
    • MorphiumCache
    • ObjectMapper
    • Query
    • Field
    • QueryFactory
    • Aggregator
    • AggregatorFactory
    • MorphiumDriver (> V3.0, for connecting to MongoDB or any other data source if you want to. For example, there is an In-Memory-Driver you might want to use for testing. As an example, there is also an InfluxDB-Driver available.)
  • Object Mapping from and to Strings (using the object mapper) and JSON.
  • full support for the Aggregation Framework
  • Transaction support (for supporting MongoDB versions)
  • Automatic encryption of fields (this is a re-implementation of the MongoDB enterprise feature in pure java - works declarative)

Advantages / Features

POJO Mapping

Morphium is capable of mapping standard Java objects (POJOs - plain old java objects) to MongoDB documents and back. This should make it possible to seemlessly integrate MongoDB into your application.

Declarative caching

When working with databases - not only NoSQL ones - you need to consider caching. Morphium integrates transparent declarative caching by entity to your application, if needed. Just define your caching needs in the @Cache annotation.(fn)

The cache uses any JavaCache compatible cache implementation (like EHCache), but provides an own implementation if nothing is specified otherwise.

There are two kinds of caches: read cache and write cache.

Write cache:

The WriteCache is just a buffer, where all things to write will be stored and eventually stored to database. This is done by adding the Annotation @WriteBuffer to the class:

@WriteBuffer(size = 150, strategy = WriteBuffer.STRATEGY.DEL_OLD)
public static class BufferedBySizeDelOldObject extends UncachedObject {


In this case, the buffer has a maximum of 150 entries, and if the buffer has reached that maximum, the oldest entries will just be deleted from buffer and hence NOT be written!

Possible strategies are:

  • WriteBuffer.STRATEGY.DEL_OLD: delete oldest entries from buffer - use with caution
  • WriteBuffer.STRATEGY.IGNORE_NEW: Do not write the new entry - just discard it. use with caution
  • WriteBuffer.STRATEGY.JUST_WARN: just log a warning message, but store data anyway
  • WriteBuffer.STRATEGY.WRITE_NEW: write the new entry synchronously and wait for it to be finished
  • WriteBuffer.STRATEGY.WRITE_OLD: write some old data NOW, wait for it to be finished, than queue new entries

That's it - rest is 100% transparent - just call; - the rest is done automatically.

internally it uses the BufferedWriter implementation, which can be changed, if needed (see configuration options below). Also, some config settings exist for switching off the buffered writing altogether - comes in handy when testing. have a closer look at the configuration options in MorphiumConfig which refer to writeBuffer or BufferedWriter.

Read Cache

Read caches are defined on type level with the annotation @Cache. There you can specify, how your cache should operate:

@Cache(clearOnWrite = true, maxEntries = 20000, strategy = Cache.ClearStrategy.LRU, syncCache = Cache.SyncCacheStrategy.CLEAR_TYPE_CACHE, timeout = 5000)
public class MyCachedEntity {

here a cache is defined, which has a maximum of 20000 entries. Those Entries have a lifetime of 5 seconds (timeout=5000). Which means, no element will stay longer than 5sec in cache. The strategy defines, what should happen, when you read additional object, and the cache is full:

  • Cache.ClearStartegy.LRU: remove least recently used elements from cache
  • Cache.ClearStrategy.FIFO:first in first out - depending time added to cache
  • Cache.ClearStrategy.RANDOM: just remove some random entries

    With clearOnWrite=true set, the local cache will be erased any time you write an entity of this typte to database. This prevents dirty reads. If set to false, you might end up with stale data (for as long as the timeout value) but produce less stress on mongo and be probably a bit faster.

cache synchronization

as mentioned above, caching is of utter importance in production grade applications. Usually, caching in a clustered Environment is kind of a pain. As you need consider dirty reads and such. But Morphium caching works also fine in a clustered environment. Just start (instantiate) a CacheSynchronizer - and you're good to go!

There are two implementations of the cache synchronizer:

  • WatchingCacheSynchronizer: uses mongodbs watch - Feature to get informed about changes in collections via push.
  • MessagingCacheSynchronizer: uses messaging to inform cluster members about changes. This one has the advantage that you can send messages manually or when other events occur

**Internals / Implementation details **

  • Morphium uses the cache based on the search query, sort options and collection overrides given. This means that there might be duplicate cache entries. In order to minimize the memory usage, Morphium also uses an ID-Cache. So all results are just added to this id cache and those ids are added as result to the query cache.

    the Caches are organized per type. This means, if your entity is not marked with @Cache, queries to this type won't be cached, even if you override the collection name.
  • The cache is implemented completely unblocking and completely thread safe. There is almost no synchronized block in Morphium.

It's a common problem, especially in clustered environments. How to synchronize caches on the different nodes. Morphium offers a simple solutions for it: On every write operation, a Message is stored in the Message queue (see MessagingSystem) and all nodes will clear the cache for the corresponding type (which will result in re-read of objects from mongo - keep that in mind if you plan to have a hundred hosts on your network) This is easy to use, does not cause a lot of overhead. Unfortunately it cannot be more efficient hence the Cache in Morphium is organized by searches.

the Morphium cache synchronizer does not issue messages for uncached entities or entities, where clearOnWrite is set to false.

Here is an example on how to use this:

    Messaging m=new Messaging(morphium,10000,true);
MessagingCacheSynchronizer cs=new MessagingCacheSynchronizer(m,morphium);

Actually this is all there is to do, as the CacheSynchronizer registers itself to both Morphium and the messaging system.

Change since 1.4.0

Now the Caching is specified by every entity in the @Cache annotation using one Enum called SyncCacheStrategy. Possible Values are: NONE (Default), CLEAR_TYPE_CACHE (clear cache of all queries on change) and UPDATE_ENTRY (updates the entry itself), REMOVE_ENTRY_FROM_TYPE_CACHE (removes all entries from cache, containing this element)


UPDATE_ENTRY only works when updating records, not on drop or remove or update (like inc, set, push...). For example, if UPDATE_ENTRY is set, and you drop the collection, type cache will be cleared.

Attention: UPDATE_ENTRY will result in dirty reads, as the Item itself is updated, but not the corresponding searches!

Meaning: assume you have a Query result cached, where you have all Users listed which have a certain role:

   Query<User> q=morphium.createQueryFor(User.class);
List<User> lst=q.asList();

Let's further assume you got 3 Users as a result. Now imagine, one node on your cluster changes the role of one of the users to something different than "Admin". If you have a list of users that might be changed while you use them! Careful with that! More importantly: your cache holds a copy of that list of users for a certain amount of time. During that time you will get a dirty read. Meaning: you will get objects that actually might not be part of your query or you will not get that actually might (not so bad actually).

Better use REMOVE_ENTRY_FROM_TYPE_CACHE in that case, as it will keep everything in cache except your search results containing the updated element. Might also cause a dirty read (as the newly added elements might not be added to your results) but it keeps findings more or less correct.

As all these synchronizations are done by sending messages via the Morphium own messaging system (which means storing messages in DB), you should really consider just disabling cache in case of heavy updates as a read from Mongo might actually be lots faster then sync of caches.

Keep that in mind!

Change since 1.3.07

Since 1.3.07 you need to add a autoSync=true to your cache annotation, in order to have things synced. It tuned out, that automatic syncing is not always the best solution. So, you can still manually sync your caches.

Manually Syncing the Caches

The sync in Morphium can be controlled totally manually (since 1.3.07), just send your own Clear-Cache Message using the corresponding method in CacheSynchronizer.

   cs.sendClearMessage(CachedObject.class,"Manual delete");


When it comes to dirty reads and such, you might want to use the auto-versioning feature of Morphium. This will give every entity a version number. If you want to write to MongoDB and the version number differs, you'd get an exception - meaning the database was modified before you tried to persist your data. This so called optimistic locking will help in most cases to avoid accidental overwriting of data.

To use auto-Versioning, just set the corresponding flag in the @Entity-annotation to true and define a Long in your class, that should hold the version number using the @Version-annotation.

Attention: do not change the version value manually, this will cause problems writing and will most probably cause loss of data!

Type IDs

usually Morphium knows which collection holds which kind of data. When de-serializing it is easy to know, what class to instanciate.

But when it comes to polymorphism and containers (like lists and maps), things get compicated. Morphium adds in this case the class name as property to the document. Up until version 4.0.0 this was causing some problems when refactoring your Entities. If you changed the classname or the package name of that class, de-serializing was impossible (the classname was obviously wrong).

now you can just set the typeId in @Entity to be able refactor more easily. If you already have data, and you want to refactor your entitiy names, just add the original class name as type id!


One of the very convenient features of SQL-Databases is the support for sequences. This is very useful when trying to have unique IDs.

Morphium implements a feature very similar to SQL-Sequences. Hence it is also called SequenceGenerator.

A sequence is a simple implementation in Morphium that uses MongoDB to generate unique numbers. Example:

SequenceGenerator sg = new SequenceGenerator(morphium, "tstseq", 1, 1);
long v = sg.getNextValue();
assert (v == 1) : "Value wrong: " + v;
v = sg.getNextValue();
assert (v == 2);

As those generators use MongoDB for synchronization, they are cluster-safe and can be used by all clients of the same MongoDB simultaneously. No number will be delivered twice!

This test here uses several Threads to access the same SequenceGenerator:

 final SequenceGenerator sg1 = new SequenceGenerator(morphium, "tstseq", 1, 0);
Vector<Thread> thr = new Vector<>();
final Vector<Long> data = new Vector<>();
for (int i = 0; i < 10; i++) {
Thread t = new Thread(() -> {
for (int i1 = 0; i1 < 25; i1++) {
long nv = sg1.getNextValue();
assert (!data.contains(nv)) : "Value already stored? Value: " + nv;
try {
} catch (InterruptedException e) {
}"Waiting for threads to finish");
for (Thread t : thr) {
long last = -1;
for (Long l : data) {
assert (last == l - 1);
last = l;

Here is an example, where the sequences are being used by a lot of separate threads each with its own connection to mongodb:

Thread.sleep(100); //wait for the drop to be persisted

//creating lots of sequences, with separate MongoDBConnections
//reading from the same sequence
//in different Threads
final Vector<Long> values=new Vector<>();
List<Thread> threads=new ArrayList<>();
final AtomicInteger errors=new AtomicInteger(0);
for (int i = 0; i < 10; i++) {
Morphium m=new Morphium(MorphiumConfig.fromProperties(morphium.getConfig().asProperties()));

Thread t=new Thread(()->{
SequenceGenerator sg1 = new SequenceGenerator(m, "testsequence", 1, 0);
for (int j=0;j<100;j++){
long l=sg1.getNextValue();"Got nextValue: "+l);
log.error("Duplicate value "+l);
} else {
try {
Thread.sleep((long) (100*Math.random()));
} catch (InterruptedException e) {

while (threads.size()>0){
//"Threads active: "+threads.size());


Attention after creating a new SequenceGenerator the currentValue will be startValue-inc in order so that getNextValue() will return startValue first.

When migrating to Morphium 4.2.x or higher from older versions the sequences will not be compatible anymore due to a change in ID.

to fix that, you need to run the following command in mongoDB shell:


transparent encryption of values

Morphium implemented a client side version of auto encrypted fields. When defining a property, you can specify the value to be encrypted. Morphium provides an implementation of AESEncryption, but you could implement any other encryption.

In order for encryption to work, we need to provide a ValueEncryptionProvider. This is a very simple interface:

        package de.caluga.morphium.encryption;

public interface ValueEncryptionProvider {
void setEncryptionKey(byte[] key);

void setEncryptionKeyBase64(String key);

void setDecryptionKey(byte[] key);

void sedDecryptionKeyBase64(String key);

byte[] encrypt(byte[] input);

byte[] decrypt(byte[] input);


There are two implementations available: AESEncryptionProvider and RSAEncryptionProvider.

Another interface being used is the EncryptionKeyProvider, a simple system for managing encryption keys:

        package de.caluga.morphium.encryption;

public interface EncryptionKeyProvider {
void setEncryptionKey(String name, byte[] key);

void setDecryptionKey(String name, byte[] key);

byte[] getEncryptionKey(String name);

byte[] getDecryptionKey(String name);


The DefaultEncrptionKeyProvider acutally is a very simple key-value-store and needs to be filled manually. The implementation PropertyEncryptionKeyProvider reads those keys from encrypted property files.

Here is an example, on how to use the transparent encryption:

public static class EncryptedEntity {
public MorphiumId id;

@Encrypted(provider = AESEncryptionProvider.class, keyName = "key")
public String enc;

@Encrypted(provider = AESEncryptionProvider.class, keyName = "key")
public Integer intValue;

@Encrypted(provider = AESEncryptionProvider.class, keyName = "key")
public Float floatValue;

@Encrypted(provider = AESEncryptionProvider.class, keyName = "key")
public List<String> listOfStrings;

@Encrypted(provider = AESEncryptionProvider.class, keyName = "key")
public Subdoc sub;

public String text;

public void objectMapperTest() throws Exception {
morphium.getEncryptionKeyProvider().setEncryptionKey("key", "1234567890abcdef".getBytes());
morphium.getEncryptionKeyProvider().setDecryptionKey("key", "1234567890abcdef".getBytes());
MorphiumObjectMapper om = morphium.getMapper();
EncryptedEntity ent = new EncryptedEntity();
ent.enc = "Text to be encrypted";
ent.text = "plain text";
ent.intValue = 42;
ent.floatValue = 42.3f;
ent.listOfStrings = new ArrayList<>();

ent.sub = new Subdoc();
ent.sub.intVal = 42;
ent.sub.strVal = "42"; = "name of the document";

//serializing the document needs to encrypt the data
Map<String, Object> serialized = om.serialize(ent);
assert (!ent.enc.equals(serialized.get("enc")));

//checking deserialization used decryption
EncryptedEntity deserialized = om.deserialize(EncryptedEntity.class, serialized);
assert (deserialized.enc.equals(ent.enc));
assert (ent.intValue.equals(deserialized.intValue));
assert (ent.floatValue.equals(deserialized.floatValue));
assert (ent.listOfStrings.equals(deserialized.listOfStrings));

Please note, that the key name used for encryption and decryption is to be defined in the property configuration of the corresponding entity.

binary serialization

the config of morphium does have a setting called objectSerializationEnabled. When set to true this will cause morphium to use the standard binary serialization of the JDK to store any instance of any class that implements serializable4.

Another setting in the config called warnOnNoEntitySerialization will create a warning message in log, when this serialization takes place.

This is set to true by default, to make development easier. But you probably do not want to use it on heavy load entities.

To store the binary data, Morphium uses a helper class called BinarySerializedObject, which will be shown in MongoDB:

"_id" : ObjectId("5f5bc1d8f8fd8247688e41f5"),
"list" : [ 
"original_class_name" : "de.caluga.test.mongo.suite.NonEntitySerialization$NonEntity",
"_b64data" : "rO0ABXNyADtkZS5jYWx1Z2EudGVzdC5tb25nby5zdWl0ZS5Ob25FbnRpdHlTZXJpYWxpemF0aW9u\r\nJE5vbkVudGl0eV18gEK68jkAAgACTAAHaW50ZWdlcnQAE0xqYXZhL2xhbmcvSW50ZWdlcjtMAAV2\r\nYWx1ZXQAEkxqYXZhL2xhbmcvU3RyaW5nO3hwc3IAEWphdmEubGFuZy5JbnRlZ2VyEuKgpPeBhzgC\r\nAAFJAAV2YWx1ZXhyABBqYXZhLmxhbmcuTnVtYmVyhqyVHQuU4IsCAAB4cAAAACp0ABZUaGFuayB5\r\nb3UgZm9yIHRoZSBmaXNo"
"Some string"

In this case, this "Container" does contain a list of non-entity objects:

public class NonEntityContainer {
private MorphiumId id;
private List<Object> list;
private HashMap<String, Object> map;

public MorphiumId getId() {
return id;

public void setId(MorphiumId id) { = id;

public List<Object> getList() {
return list;

public void setList(List<Object> list) {
this.list = list;

public HashMap<String, Object> getMap() {
return map;

public void setMap(HashMap<String, Object> map) { = map;

public class NonEntity implements Serializable {
private String value;
private Integer integer;

public String getValue() {
return value;

public void setValue(String value) {
this.value = value;

public Integer getInteger() {
return integer;

public void setInteger(Integer integer) {
this.integer = integer;

public String toString() {
return "NonEntity{" +
"value='" + value + '\'' +
", integer=" + integer +

Attention: please keep in mind, that you cannot store non-entities directly. Only a member variable of an entity (even if it is in a list or Map) might be non-entities.

complex data structures

In the jUnit tests, Morphium is tested to support those complex data structures, like lists of lists, lists of maps or maps of lists of entities. I think, you'll get the picture:

  public static class CMapListObject extends MapListObject {
private Map<String, List<EmbObj>> map1;
private Map<String, EmbObj> map2;
private Map<String, List<String>> map3;
private Map<String, List<EmbObj>> map4;

private Map<String, Map<String, String>> map5;
private Map<String, Map<String, EmbObj>> map5a;
private Map<String, List<Map<String, EmbObj>>> map6a;

private List<Map<String, String>> map7;
private List<List<Map<String, String>>> map7a;

have a look at the Tests in code on github for more examples. the main challenge here is, to determine the right type of elements in the list in order to be able to de-serialize them properly. In this case, de-serialization is done in background transparently:

public void testListOfListOfMap() {

CMapListObject o = new CMapListObject();
List<List<Map<String, String>>> lst = new ArrayList<>();
List<Map<String, String>> l2 = new ArrayList<>();
Map<String, String> map = new HashMap<>();
map.put("k1", "v1");
map.put("k2", "v2");
map = new HashMap<>();
map.put("k11", "v11");
map.put("k21", "v21");
map.put("k31", "v31");

l2 = new ArrayList<>();
map = new HashMap<>();
map.put("k15", "v1");
map.put("k25", "v2");
map = new HashMap<>();
map.put("k51", "v11");
map.put("k533", "v21");
map.put("k513", "v31");
map = new HashMap<>();
map.put("k512", "v11");
map.put("k514", "v21");
map.put("k513", "v31");


CMapListObject ml = morphium.findById(CMapListObject.class, o.getId());
assert (ml.getMap7a().get(1).get(0).get("k15").equals("v1"));

as you see here, the deserialization is done transparently in background even on several levels "down", the CMapListObject is initialized properly.

Caveat: this can only work, if java knows the type of the elements in the list. As soon as there is a List<Object> in the type definition, morphium does not know, what the type might be. It will try to deserialize it (which will work if it is a proper entity), but might not work in all cases. If this detection fails, you'll likely end up getting a ClassCastException. If so, try to define the data structure more strictly or simplify it.

Support for MapReduce

To do complex aggregations and analysis of your data in MongoDB the first choice to do that was MapReduce. If necessary or convenient, you can use that with Morphium as well, although it is not as powerful as the Aggregation Framework (see below).

Here is a basic example on how to use MapReduce:

private void doSimpleMRTest(Morphium m) throws Exception {
List<UncachedObject> result = m.mapReduce(UncachedObject.class, "function(){emit(this.counter%2==0,this);}", "function (key,values){var ret={_id:ObjectId(), value:\"\", counter:0}; if (key==true) {ret.value=\"even\";} else { ret.value=\"odd\";} for (var i=0; i<values.length;i++){ret.counter=ret.counter+values[i].counter;}return ret;}");
assert (result.size() == 2);
boolean odd = false;
boolean even = false;
for (UncachedObject r : result) {
if (r.getValue().equals("odd")) {
odd = true;
if (r.getValue().equals("even")) {
even = true;
assert (r.getCounter() > 0);
assert (odd);
assert (even);

the problem here is, that you need to write JavaScript code and hence need to switch between contexts, whereas the Aggregation support in Morphium lets you define the whole pipeline in Java.

automatic retries on error

The write concern aka WriteSafety-Annotation in Morphium is not enough for being on the safe side. the WriteSafety only makes sure, that, if all is ok, data is written to the amount of nodes, you want it to be written. You define the safety level more or less in an Application point of view. This does not affect networking outage or other problems. Also in case of a failover during access, you will end up with an exception in application. In order to deal with the problem, the coding advice for MongoDB is, to have all accesses run in a loop so that you can retry on failure and hope for fast recovery.

Morphium takes care of that: all access to mongo is done in a loop and Morphium tries to detect if that error is recoverable (like a failover) or not. there are several retry-settings in the config.

retry settings in writers

Morphium has 3 different types of writers:

  • the normal writer: supports asynchronous and synchronous writes
  • the async writer: forces asynchronous writes
  • the buffered writer: stores write requests in a buffer and executes those on block

This has some implications, as the core of Morphium is asynchronous, we need to make sure, there are not too many pending writes. (the "pile" is determined by the maximum amount of connections to mongo - hence this is something you won't need to configure)

This is where the retry settings for writers come in. When writing data, this data is either written synchronously or asynchronously. In the latter case, the requests tend to pile up on heavy load. And we need to handle the case, when this pile gets too high. This is the retry. When the pile of pending requests is too high, wait for a specified amount of time and try again to queue the operation. If that fails for all retries - throw an exception.

Retry settings for Network errors

As we had a really sh... network which causes problems more than once a day, we needed to come up with a solution for this as well. As our network does not fail for more than a couple of requests, the idea is to detect network problems and retry the operation after a certain amount of time. This setting is specified globally in Morphium config:




This causes Morphium to retry any operation on mongo 10 times (if a network related error occurs) and pause 500ms between each try. This includes, reads, writes, updates, index creation and aggregation. If the access failed after the (in this case) 10th try - rethrow the networking error to the caller.

configuring Morphium: MorphiumConfig

MorphiumConfig is the class to encapsulate all settings for Morphium. The most obvious settings are the host seed and port definitions. But there is a ton of additional settings available.

Different sources


The standard toString()method of MorphiumConfig creates an Json String representation of the configuration. to set all configuration options from a json string, just call createFromJson.


the configuration can be stored and read from a property object.

MorphiumConfig.fromProperties(Properties p); Call this method to set all values according to the given properties. You also can pass the properties to the constructor to have it configured.

To get the properties for the current configuration, call asProperties() on a configured MorphiumConfig Object.

Here is an example property-file:

hosts=localhost\:27017, localhost\:27018, localhost\:27019

The minimal property file would define only hosts and database. All other values would be defaulted.

If you want to specify classes in the config (like the Query Implementation), you need to specify the full qualified class name, e.g. de.caluga.morphium.customquery.QueryImpl


The most straight forward way of configuring Morphium is, using the object directly. This means you call the getters and setters according to the given variable names above (like setMaxAutoReconnectTime()).

The minimum configuration is explained above: you only need to specify the database name and the host(s) to connect to. All other settings have sensible defaults, which should work for most cases.

Configuration Options

There are a lot of settings and customizations you can do within Morphium. Here we discuss all of them:

  • loggingConfigFile: can be set, if you want Morphium to configure your log4j for you. Morphium itself has a dependency to log4j (see Dependencies).
  • camelCaseConversion: if set to false, the names of your entities (classes) and fields won't be converted from camelcase to underscore separated strings. Default is true (convert to camelcase)
  • maxConnections: Maximum Number of connections to be built to mongo, default is 10
  • houseKeepingTimeout: the timeout in ms between cache housekeeping runs. Defaults to 5sec
  • globalCacheValidTime: how long are Cache entries valid by default in ms. Defaults to 5sek
  • writeCacheTimeout: how long to pause between buffered writes in ms. Defaults to 5sek
  • database: Name of the Database to connect to.
  • connectionTimeout: Set a value here (in ms) to specify how long to wait for a connection to mongo to be established. Defaults to 0 (Ôçĺ infinite)
  • socketTimeout: how long to wait for sockets to be established, defaults to 0 as well
  • checkForNew: This is something interesting related to the creation of ids. Usually Ids in mongo are of type ObjectId. Anytime you write an object with an _id of that type, the document is either updated or inserted, depending on whether or not the ID is available or not. If it is inserted, the newly created ObjectId is being returned and add to the corresponding object. But if the id is not of type ObjectId, this mechanism will fail, no objectId is being created. This is no problem when it comes to new creation of objects, but with updates you might not be sure, that the object actually is new or not. If this obtion is set to true Morphium will check upon storing, whether or not the object to be stored is already available in database and would update.
  • writeTimeout: this timeout determines how long to wait until a write to mongo has to be finshed. Default is 0Ôçĺ no timeout
  • maximumRetriesBufferedWriter: When writing buffered, how often should retry to write the data until an exception is thrown. Default is 10
  • retryWaitTimeBufferedWriter: Time to wait between retries
  • maximumRetriesWriter, maximumRetriesAsyncWriter: same as maximumRetriesBufferedWriter, but for direct storage or asynchronous store operation.
  • retryWaitTimeWriter, retryWaitTimeAsyncWriter: similar to retryWaitTimeBufferedWriter, but for the according writing type
  • globalW: W sets the number of nodes to have finished the write operation (according to your safe and j / fsync settings)
  • maxWaitTime: Sets the maximum time that a thread will block waiting for a connection.
  • serverSelectionTimeout: Defines how long the driver will wait for server selection to succeed before throwing an exception
  • writeBufferTime: Timeout for buffered writes. Default is 0
  • autoReconnect: if set to true connections are re-established, when lost. Default is true
  • maxAutoReconnectTime: how long to try to reconnect (in ms). Default is 0Ôçĺ try as long as it takes
  • mongoLogin,mongoPassword: User Credentials to connect to MongoDB. Can be null.
  • mongoAdminUser, mongoAdminPwd: Credentials to do admin tasks, like get the replicaset status. If not set, use mongoLogin instead.
  • autoValuesEnabled: Morphium supports automatic values being set to your POJO. These are configured by annotations (@LasChange, @CreationTime, @LastAccess, ...). If you want to switch this off globally, you can set it in the config. Very useful for test environments, which should not temper with productional data. By default the auto values are enabled.
  • readCacheEnabled: Globally enable or disable readcache. This only affects entities with a @Cache annotation. By default it's enabled.
  • asyncWritesEnabled: Globally enable or disalbe async writes. This only affects entities with a @AsyncWritesannotation
  • bufferedWritesEnabled: Globally enable or disable buffered writes. This only affects entities with a @WriteBuffer annotation
  • defaultReadPreference: whether to read from primary, secondary or nearest by default. Can be defined with the @ReadPreference annotation for each entity.
  • replicaSetMonitoringTimeout: time interval to update replicaset status.
  • retriesOnNetworkError: if you happen to have an unreliable network, maybe you want to retry writes / reads upon network error. This settings sets the number of retries for that case.
  • sleepBetweenNetworkErrorRetries: set the time to wait between network error retries.
  • autoIndexAndCappedCreationOnWrite: This setting is by default true which means, that Morphium keeps a list of existing collections. When a collection would be created automatically by writing to it, Morphium can then and only then have all indexes and capped settings configured for that specific collection. Causes a little overhead on write access to see, if a collection exists. Probably a good idea to switch off in production environment, but for development it makes things easier.

In addition to those settings describing the behaviour of Morphium, you can also define custom classes to be used internally:

  • omClass: here you specify the class, that should be used for mapping POJOs (your entities) to Documnet. By Default it uses the ObjectMapperImpl. Your custom implementation must implement the interface ObjectMapper.
  • iteratorClass: set the Iterator implementation to use. By default MorphiumIteratorImplis being used. Your custom implementation must implement the interface MorphiumIterator
  • aggregatorClass: this is Morphium's representation of the aggregator framework. This can be replaced by a custom implementation if needed. Implements Aggregator interface
  • aggregatorFactoryClass: this is Morphium's representation of the aggregator framework. This can be replaced by a custom implementation if needed. Implements AggregatorFactory interface
  • queryClass and fieldImplClass: this is used for Queries. If you want to take control over how queries ar built in Morphium and on how fields within queries are represented, you can replace those two with your custom implementation.
  • queryFactoryClass: query factory implementation, usually just creates a Query-Object. Custom implementations need to implement the QueryFactory interface.
  • cache: Set your own implementation of the cache. It needs to implement the MorphiumCache interface. Default is MorphiumCacheImpl. You need to specify a fully configured cache object here, not only a class object.
  • driverClass: Set the driver implementation, you want to use. This is a string, set the class name here. E.g. MorphiumConfig.setDriverClass(MetaDriver.class.getName(). Custom implementations need to implement the MorphiumDriver interface. By default the MongodbDriver is used, which connects to mongo using the official Java driver. But there are some other implementations, that do have some advantages (like the inMemoryDriver or the ones from the project here.

In Mongo until V 2.4 authentication and user privileges were not really existent. With 2.4, roles are introduces which might make it a bit more complicated to get things working.


Morphium supports authentication, of course, but on startup. So usually you have an application user, which connects to database. Login to mongo is configured as follows:

    MorphiumConfig cfg=new Morpiumconfig(...);

This user usually needs to have read/write access to the database. If you want your indices to be created automatically by you, this user also needs to have the role dbAdmin for the corresponding database. If you use Morphium with a replicaset of mongo nodes, Morphium needs to be able to get access to local database and get the replicaset status. In order to do so, either the mongo user needs to get additional roles (clusterAdmin and read to local db), or you specify a special user for that task, which has excactly those roles. Morphium authenticates with that different user for accessing replicaSet status (and only for getting the replicaset status) and is configured very similar to the normal login:


corresponding MongoD Config

You need to run your mongo nodes with -auth (or authenticate = true set in config) and if you run a replicaset, those nodes need to share a key file or kerberos authentication. (see Let's assume, that all works for now. Now you need to specify the users. One way of doing that is the following:

  • add the user for mongo to your main database (in our case tst)

  • add an admin user for your own usage from shell to admin db (with all privileges)

  • add the clusterAdmin user to admin db as well, grant read access to local

    use admin
    use morphium_test

    Here morphium_test is your application database Morphium is connected to primarily. The admin db is a system database.

This is far away from being a complete guide, I hope this just gets you started with authentication....

Entity Definition

Entities in Morphium are just "Plain old Java Objects" (POJOs). So you just create your data objects, as usual. You only need to add the annotation @Entity to the class, to tell Morphium "Yes, this can be stored". The only additional thing you need to take care of is the definition of an ID-Field. This can be any field in the POJO identifying the instance. Its best, to use ObjectID as type of this field, as these can be created automatically and you don't need to care about those as well.

If you specify your ID to be of a different kind (like String), you need to make sure, that the String is set, when the object will be written. Otherwise you might not find the object again. So the shortest Entity would look like this:

public class MyEntity {
@Id private ObjectId id;
//.. add getter and setter here


Indexes are critical in mongo, so you should definitely define your indexes as soon as possible during your development. Indexes can be defined on the Entity itself, there are several ways to do so: - @Id always creates an index - you can add an @Index to any field to have that indexed:


private String name;

you can define combined indexes using the @Index annotation at the class itself:

        @Index({"counter, name","value,thing,-counter"}
public class MyEntity {

This would create two combined indexes: one with counter and name (both ascending) and one with value, thing and descending counter. You could also define single field indexes using this annotations, but it's easier to read adding the annotation directly to the field.

Indexes will be created automatically if you create the collection. If you want the indexes to be created, even if there is already data stores, you need to call morphium.ensureIndicesFor(MyEntity.class)- You also may create your own indexes, which are not defined in annotations by calling morphium.ensureIndex(). As parameter you pass on a Map containing field name and order (-1 or 1) or just a prefixed list of strings (like"-counter","name").

Every Index might have a set of options which define the kind of this index. Like buildInBackground or unique. You need to add those as second parameter to the Index-Annotation:

@Index(value = {"-name, timer", "-name, -timer", "lst:2d", "name:text"}, 
options = {"unique:1", "", "", ""})
public static class IndexedObject {

here 4 indexes are created. The first two are more or less standard, wheres the lst index is a geospatial one and the index on name is a text index (only since mongo 2.6). If you need to define options for one of your indexes, you need to define it for all of them (here, only the first index is unique).

Text indexes

MongoDB has a built in text search functionality since V3.x. This can be used in command line, or using Morphium. In order for it to work, a text index needs to be defined for the entity/collection. Here an example for an entity called Person:

@Index(value = {"vorname:text,nachname:text,anrede:text,description:text", "age:1"}, options = {"name:myIdx"})
public static class Person { 
//properties and getters/setters left out for readability

in this case, a text index was built on fields vorname, nachname, andrede and description.

To use the index, we need to create a text query5:

public void textIndexTest() throws Exception {
try {
} catch (Exception e) {"Text search not enabled - test skipped");
Query<Person> p = morphium.createQueryFor(Person.class);
List<Person> lst = p.text(Query.TextSearchLanguages.english, "hugo", "bruce").asList();
assert (lst.size() == 2) : "size is " + lst.size();
p = morphium.createQueryFor(Person.class);
lst = p.text(Query.TextSearchLanguages.english, false, false, "Hugo", "Bruce").asList();
assert (lst.size() == 2) : "size is " + lst.size();

In this case, there is some Data created, which puts the name of some superheroes in a mongo. Searching for the text ist something different than searching via regular expressions, because Text Indexes are way more efficient in that case.

If you need more information on text indexes, have a look at MongoDBs documentation and take a look at the Tests for TextIndexes within the source code of Morphium.

capped collections

Similar as with indexes, you can define you collection to be capped using the @Capped annotation. This annotation takes two arguments: the maximum number of entries and the maximum size. If the collection does not exist, it will be created as capped collection using those two values. You can always ensureCapped your collection, unfortunately then only the size parameter will be honoured.


Querying is done via the Query-Object, which is created by Morphium itself (using the Query Factory). The definition of the query is done using the fluent interface:

Query<MyEntity> query=_Morphium_.createQueryFor(MyEntity.class);
query=query.f("id").eq(new ObjectId());
query=query.f("valueField").eq("the value");

In this example, I refer to several fields of different types. The Query itself is always of the same basic syntax:

queryObject=queryObject.skip(NUMBER); //skip a number of entreis
queryObject=queryObject.limig(NUMBER); // limit result

As field name you may either use the name of the field as it is in mongo or the name of the field in java. If you specify an unknown field to Morphium, a RuntimeException will be raised.

For definition of the query, it's also a good practice to define enums for all of your fields. This makes it hard to have mistypes in a query:

        public class MyEntity {
//.... field definitions
public enum Fields { id, value, personName,counter, }

There is a IntelliJ plugin ("GeneratePropertyEnums") that is used for creating those enums automatically. Then, when defining the query, you don't have to type in the name of the field, just use the field enum:


This avoids typos and shows compile time errors, when a field was renamed for whatever reason.

After you defined your query, you probably want to access the data in mongo. Via Morphium,there are several possibilities to do that: - queryObject.get(): returns the first object matching the query, only one. Or null if nothing matched - queryObject.asList(): return a list of all matching objects. Reads all data in RAM. Useful for small amounts of data - Iterator<MyEntity> it=queryObject.asIterator(): creates a MorphiumIterator to iterate through the data, which does not read all data at once, but only a couple of elements in a row (default 10).

Simple queries

most of your queries probably are simple ones. like searching for a special id or value. This is done rather simply with the query-Object: morphium.createQueryFor(MyEntity.class).f("field").eq(value) if you add more f(fields) to the query, they will be concatenated by a logical AND. so you can do something like:

    Query<UncachedObject> q=morphium.createQueryFor(UncachedObject.class);

This would result in a query like: "All Uncached Objects, where counter is greater than 10 and counter is less then 20".

Or Queries

in addition to those AND-queries you can add an unlimited list of queries to it, which will be concatenated by a logical OR.

   q.f("counter").lt(100).or(q.q().f("value").eq("Value 12"), q.q().f("value").eq("other"));

This would create a query like: "all UncachedObjects where counter is less than 100 and (value is 'value 12' or value is 'other')"

the Method q() creates a new empty query for the same object. It's a convenience Method. Please be careful, never use your query Object in the parameter list of or - this would cause and endless loop! ATTENTION here!

This gives you the possibility to create rather complex queries, which should handle about 75% of all cases. Although you can also add some NOR-Queries as well. These are like "not or"-Queries....

   q.f("counter").lt(100).nor(q.q().f("counter").eq(90), q.q().f("counter").eq(55));

this would result in a query like: "All query objects where counter is less than 100 and not (counter=90 or counter=55).

this adds another complexity level to the queries ;-)

If that's not enough, specify your own query in "mongo"-Syntax.

You can also specify your own query object (Map<String,Object>) in case of a very complex query. This is part of the Query-Object and can be used rather easily:

        Map<String,Object> query=new HashMap<>();
Query<UncachedObject> q=MorphiumSingleton.get().createQueryFor(UncachedObject.class);
List<UncachedObject> lst=q.complexQuery(query);

Although, in this case the query is a very simple one (counter < 10), but I think you get the Idea....


Well, the fluent query interface does have its limitations. So its not possible to have a certain number of or-concatenated queries (like (counter 14 or Counter <10) and (counter >50 or counter 30)). I'm not sure, this is very legible...

the Iterator

Morphium has support for a special Iterator, which steps through the data, a couple of elements at a time. By Default this is the standard behaviour. But the _Morphium_Iterator ist quite capable:

  • queryObject.asIterable() will stepp through the result list, 10 at a time
  • queryObject.asIterable(100) will step through the result list, 100 at a time
  • queryObject.asIterable(100,5) will step through the result list, 100 at a time and keep 4 chunks of 100 elements each as prefetch buffers. Those will be filled in background.
  • MorphiumIterator it=queryObject.asIterable(100,5); it.setmultithreadedAccess(true); use the same iterator as before, but make it thread safe.


Problem is, when dealing with huge tables or lots of data, you'd probably include paging to your queries. You would read data in chunks of for example 100 objects to avoid memory overflows. This is now available by Morphium. The new MorphiumIterator works as Iterable or Iterator - whatever you like. It's included in the Query-interface and can be used very easily:

Query<Type> q=morphium.createQueryFor(Type.class);
q=q.f("field").eq..... //whatever

for (Type t:q.asIterable()) {
//do something with t

This creates an iterator, reading all objects from the query in chunks of 10... if you want to read them one by one, you only ned to give the chunk-size to the call:

for (Type t:q.asIterable(1)) {
//now reads every single Object from db

You can also use the iterator as in the "good ol' days".

   Iterator<Type> it=q.asIterable(100);  //reads objects in chunks of 100
while (it.hasNext()) {
... //do something

If you use the MorphiumIterator as the type it actually is, you'd get even more information:

   MorphiumIterator<Type> it=q.asIterable(100);;
long count=it.getCount(); //returns the number of objects to be read
int cursorPos=it.getCursor(); //where are we right now, how many did we read
it.ahead(5); //jump ahead 5 objects
it.back(4); //jump back 
int bufferSize=it.getCurrentBufferSize(); //how many objects are currently stored in RAM
List<Type> lst=it.getCurrentBuffer(); //get the objects in RAM

Attention: the count is the number of objects matching the query at the instanciation of the iterator. This ensures, that the iterator terminates. The Query will be executed every time the buffer boundaries are reached. It might cause unexpected results, if the sort of the query is wrong.

For example:

   //created Uncached Objects with counter 1-100; value is always "v"
Query<UncachedObject> qu=morphium.createQueryFor(UncachedObject.class).sort("-counter");
for (UncachedObject u:qu.asIterable()) {
UncachedObject uc=new UncachedObject();
MorphiumSingleton.get().store(uc);"Current Counter: "+u.getCounter()+" and Value: "+u.getValue());

The output is as follows:

14:21:10,494 INFO  [main] IteratorTest: Current Counter: 100 and Value: v
14:21:10,529 INFO  [main] IteratorTest: Current Counter: 99 and Value: v
14:21:10,565 INFO  [main] IteratorTest: Current Counter: 98 and Value: v
14:21:10,610 INFO  [main] IteratorTest: Current Counter: 97 and Value: v
14:21:10,645 INFO  [main] IteratorTest: Current Counter: 96 and Value: v
14:21:10,680 INFO  [main] IteratorTest: Current Counter: 95 and Value: v
14:21:10,715 INFO  [main] IteratorTest: Current Counter: 94 and Value: v
14:21:10,751 INFO  [main] IteratorTest: Current Counter: 93 and Value: v
14:21:10,786 INFO  [main] IteratorTest: Current Counter: 92 and Value: v
14:21:10,822 INFO  [main] IteratorTest: Current Counter: 91 and Value: v
14:21:10,857 INFO  [main] IteratorTest: Current Counter: 96 and Value: WRONG!
14:21:10,892 INFO  [main] IteratorTest: Current Counter: 95 and Value: v
14:21:10,927 INFO  [main] IteratorTest: Current Counter: 95 and Value: WRONG!
14:21:10,963 INFO  [main] IteratorTest: Current Counter: 94 and Value: v
14:21:10,999 INFO  [main] IteratorTest: Current Counter: 94 and Value: WRONG!
14:21:11,035 INFO  [main] IteratorTest: Current Counter: 93 and Value: v
14:21:11,070 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,105 INFO  [main] IteratorTest: Current Counter: 92 and Value: v
14:21:11,140 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:11,175 INFO  [main] IteratorTest: Current Counter: 91 and Value: v
14:21:11,210 INFO  [main] IteratorTest: Current Counter: 94 and Value: WRONG!
14:21:11,245 INFO  [main] IteratorTest: Current Counter: 94 and Value: WRONG!
14:21:11,284 INFO  [main] IteratorTest: Current Counter: 93 and Value: v
14:21:11,328 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,361 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,397 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,432 INFO  [main] IteratorTest: Current Counter: 92 and Value: v
14:21:11,467 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:11,502 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:11,538 INFO  [main] IteratorTest: Current Counter: 91 and Value: v
14:21:11,572 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,607 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,642 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,677 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,713 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,748 INFO  [main] IteratorTest: Current Counter: 92 and Value: v
14:21:11,783 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:11,819 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:11,853 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:11,889 INFO  [main] IteratorTest: Current Counter: 91 and Value: v
14:21:11,923 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,958 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:11,993 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:12,028 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:12,063 INFO  [main] IteratorTest: Current Counter: 92 and Value: v
14:21:12,098 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,133 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,168 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,203 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,239 INFO  [main] IteratorTest: Current Counter: 91 and Value: v
14:21:12,273 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:12,308 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:12,344 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:12,379 INFO  [main] IteratorTest: Current Counter: 92 and Value: v
14:21:12,413 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,448 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,487 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,521 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,557 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,592 INFO  [main] IteratorTest: Current Counter: 91 and Value: v
14:21:12,626 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:12,662 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:12,697 INFO  [main] IteratorTest: Current Counter: 92 and Value: v
14:21:12,733 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,769 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,804 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,839 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,874 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,910 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:12,945 INFO  [main] IteratorTest: Current Counter: 91 and Value: v
14:21:12,980 INFO  [main] IteratorTest: Current Counter: 93 and Value: WRONG!
14:21:13,015 INFO  [main] IteratorTest: Current Counter: 92 and Value: v
14:21:13,051 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,085 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,121 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,156 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,192 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,226 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,262 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,297 INFO  [main] IteratorTest: Current Counter: 91 and Value: v
14:21:13,331 INFO  [main] IteratorTest: Current Counter: 92 and Value: v
14:21:13,367 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,403 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,446 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,485 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,520 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,556 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,592 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,627 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,662 INFO  [main] IteratorTest: Current Counter: 91 and Value: v
14:21:13,697 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,733 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,768 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,805 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,841 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,875 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,911 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,946 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:13,982 INFO  [main] IteratorTest: Current Counter: 92 and Value: WRONG!
14:21:14,017 INFO  [main] IteratorTest: Current Counter: 91 and Value: v
14:21:14,017 INFO  [main] IteratorTest: Cleaning up...
14:21:14,088 INFO  [main] IteratorTest: done...

The first chunk is ok, but all that follow are not. Fortunately count did not change or in this case, the iterator would never stop. Hence, if your collection changes while you're iterating over it, you might get inexpected results. Writing to the same collection within the loop of the iterator is generally a bad idea...

Advanced Features

Since V2.2.5 the Morphium iterator supports lookahead (prefetching). This means its not only possible to define a window size to step through your data, but also how many of those windows should be prefetched, while you step through the first one.

This works totally transparent for the user, its just a simple call to activate this feature:

theQuery.asIterable(1000,5); //window size 1000, 5 windows prefetch

Since 2.2.5 the Morphium iterator is also able to be used by multiple threads simultaneously. This means, several threads access the same iterator. This might be useful for querying and alike.

To use that, you only need to set setMultithreaddedAccess to true in the iterator itself:

MorphiumIterator<MyEntity> it=theQuery.asIterable(1000,15)

Attention: Setting mutlithreaddedAccess to true will cause the iterator to be a bit slower as it has to do some things in a synchronized fashion.


Storing is more or less a very simple thing, just call and you're done. Although there is a bit more to it: - if the object does not have an id (id field is null), there will be a new entry into the corresponding collection. - if the object does have an id set (!= null), an update to db is being issued. - you can call _Morphium_.storeList(lst) where lst is a list of entities. These would be stored in bulkd, if possible. Or it does a bulk update of things in mongo. Even mixed lists (update and inserts) are possible. Morphium will take care of sorting it out - there are additional methods for writing to mongo, like update operations set, unset, push, pull and so on (update a value on one entity or for all elements matching a query), delete objects or objects matching a query, and a like - The writer that acutally writes the data, is chosen depending on the configuration of this entity (see Annotations below)

Names of entities and fields

Morphium by defaults converts all java CamelCase identifiers in underscore separated strings. So, MyEntity will be stored in an collection called my_entity and the field aStringValue would be stored in as a_string_value.

When specifying a field, you can always use either the transformed name or the name of the corresponding java field. Collection names are always determined by the classname itself.

CamelCase conversion

But in Morphium you can of course change that behaviour. Easiest way is to switch off the transformation of CamelCase globally by setting camelCaseConversionEnabled to false (see above: Configuration). If you switch it off, its off completely - no way to do switch it on for just one collection or so.

If you need to have only several types converted, but not all, you have to have the conversion globally enabled, and only switch it off for certain types. This is done in either the @Entity or @Embedded annotation.

public class MyEntity {
private String myField;

This example will create a collection called MyEntity (no conversion) and the field will be called myField in mongo as well (no conversion).

Attention: Please keep in mind that, if you switch off camelCase conversion globally, nothing will be converted!

using the full qualified classname

you can tell Morphium to use the full qualified classname as basis for the collection name, not the simple class name. This would result in createing a collection de_caluga_morphium_my_entity for a class called de.caluga.morphium.MyEntity. Just set the flag useFQN in the entity annotation to true.

public class MyEntity {

Recommendation is, not to use the full qualified classname unless it's really needed.

Specifying a collection / fieldname

In addition to that, you can define custom names of fields and collections using the corresponding annotation (@Entity, @Property).

For entities you may set a custom name by using the collectionName value for the annotation:

public class MyEntity {
private String myValue;

the collection name will be totallyDifferent in mongo. Keep in mind that camel case conversion for fields will still take place. So in that case, the field name would probably be my_value. (if camel case conversion is enabled in config)

You can also specify the name of a field using the property annotation:

private String something;

Again, this only affects this field (in this case, it will be called my_wondwerful_field in mongo) and this field won't be converted camelcase. This might cause a mix up of cases in your MongoDB, so please use this with care.

Accessing fields

When accessing fields in Morphium (especially for the query) you may use either the name of the Field in Java (like myEntity) or the converted name depending on the config (camelCased or not, or custom).

Using NameProviders

In some cases it might be necessary to have the collection name calculated dynamically. This can be achieved using the NameProvider Interface.

You can define a NameProvider for your entity in the @Entity annotation. You need to specify the type there. By default, the NameProvider for all Entities is DefaultNameProvider. Which actually looks like this:

public final class DefaultNameProvider implements NameProvider {

public String getCollectionName(Class<?> type, ObjectMapper om, boolean translateCamelCase, boolean useFQN, String specifiedName, _Morphium_ _Morphium_) {

String name = type.getSimpleName();

if (useFQN) {
name = type.getName().replaceAll("\\.", "_");
if (specifiedName != null) {
name = specifiedName;
} else {
if (translateCamelCase) {
name = _Morphium_.getARHelper().convertCamelCase(name);
return name;

You can use your own provider to calculate collection names depending on time and date or for example depending on the querying host name (like: create a log collection for each server separately or create a collection storing logs for only one month each).

Attention: Name Provider instances will be cached, so please implement them thread safe.


mongo is really fast and stores a lot of date in no time. Sometimes it's hard then, to get this data out of mongo again, especially for logs this might be an issue (in our case, we had more than a 100 million entries in one collection). It might be a good idea to change the collection name upon some rule (by date, timestamp whatever you like). Morphium supports this using a strategy-pattern.

public class DatedCollectionNameProvider implements NameProvider{
public String getCollectionName(Class<?> type, ObjectMapper om, boolean translateCamelCase, boolean useFQN, String specifiedName, Morphium morphium) {
SimpleDateFormat df=new SimpleDateFormat("yyyyMM");
String date=df.format(new Date());
String ret=null;
if (specifiedName!=null) {
} else {
String name = type.getSimpleName();
if (useFQN) {
if (translateCamelCase) {
return ret;

This would create a monthly named collection like "my_entity_201206". In order to use that name provider, just add it to your @Entity-Annotation:

@Entity(nameProvider = DatedCollectionNameProvider.class)
public class MyEntity {


The name provider instances themselves are cached for each type upon first use, so you actually might do as much work as possible in the constructor.

BUT: on every read or store of an object the corresponding name provider method getCollectionName is called, this might cause Performance drawbacks, if you logic in there is quite heavy and/or time consuming.

Automatic values

This is something quite common: you want to know, when your data was last changed and maybe who did it. Usually you keep a timestamp with your object and you need to make sure, that these timestamps are updated accordingly. Morphium does this automatically - just declare the annotations:

public static class TstObjLA {
private ObjectId id;

private long lastAccess;

private long lastChange;

private long creationTime;

private String value;

public long getLastAccess() {
return lastAccess;

public void setLastAccess(long lastAccess) {
this.lastAccess = lastAccess;

public long getLastChange() {
return lastChange;

public void setLastChange(long lastChange) {
this.lastChange = lastChange;

public long getCreationTime() {
return creationTime;

public void setCreationTime(long creationTime) {
this.creationTime = creationTime;

public String getValue() {
return value;

public void setValue(String value) {
this.value = value;

You might ask, why do we need to specify, that access time is to be stored for the class and the field. The reason is: Performance! In order to search for a certain annotation we need to read all fields of the whole hierarchy the of the corresponding object which is rather expensive. In this case, we only search for those access fields, if necessary. All those are stored as long - System.currentTimeMillies()


@LastAccess: Stores the last time, this object was read from db! Careful with that one: it will create a write access, for every read!

@CreationTime: Stores the creation timestamp

@LastChange: Timestamp the last moment, this object was stored.

Asynchronous API

All writer implementation support asynchronous calls like

   public <T> void store(List<T> lst, AsyncOperationCallback<T> callback); 

if callback==null the method call should be synchronous... If callback!=null do the call to mongo asynchronous in background. Usually, you specify the default behaviour in your class definition:

public class EntityType {

All write operations to this type will be asynchronous! (synchronous call is not possible in this case!).

Asynchronous calls are also possible for Queries, you can call q.asList(callback) if you want to have this query be executed in background.

Difference asynchronous write / write buffer

Asynchronous calls will be issued at once to the mongoDb but the calling thread will not have to wait. It will be executed in Background. the @WriteBuffer annotation specifies a write buffer for this type (you can specify the size etc if you like). All writes will be held temporarily in ram until time frame is reached or the number of objects in write buffer exceeds the maximum you specified (0 means no maximum). Attention if you shut down the Java VM during that time, those entries will be lost. Please only use that for logging or "not so important" data. specifying a write buffer four you entitiy is quite easy:

@WriteBuffer(size=1000, timeout=5000)
public class MyBufferedLog {

This means, all write access to this type will be stored for 5 seconds or 1000 entries, whichever occurs first. If you want to specify a different behavior when the maximum number of entries is reached, you can specify a strategy:

  • WRITE_NEW: write newest entry (synchronous and not add to buffer)
  • WRITE_OLD: write some old entries (and remove from buffer)
  • DEL_OLD: delete old entries from buffer - oldest elements won't be written to Mongo!
  • IGNORE_NEW: just ignore incoming - newest elements WILL NOT BE WRITTEN!
  • JUST_WARN: increase buffer and warn about it

Validation support

Morphium does support for javax.validation annotations and those might be used to ensure data quality:

private MorphiumId id;

private int theInt;

private Integer anotherInt;

private Date whenever;

@Pattern(regexp = "m[ue├╝]nchen")
private String whereever;

@Size(min = 2, max = 5)
private List friends;

private String email;

You do not need to have any validator implementation in classpath, Morphium detects, if validation is available and only enables it then.


a lot of things can be configured in Morphium using annotations. Those annotations might be added to either classes, fields or both.


Perhaps the most important Annotation, as it has to be put on every class the instances of which you want to have stored to database. (Your data objects).

By default, the name of the collection for data of this entity is derived by the name of the class itself and then the camel case is converted to underscore strings (unless config is set otherwise).

These are the settings available for entities:

  • translateCamelCase: default true. If set, translate the name of the collection and all fields (only those, which do not have a custom name set)
  • collectionName: set the collection name. May be any value, camel case won't be converted.
  • useFQN: if set to true, the collection name will be built based on the full qualified class name. The Classname itself, if set to false. Default is false
  • polymorph: if set to true, all entities of this type stored to mongo will contain the full qualified name of the class. This is necessary, if you have several different entities stored in the same collection. Usually only used for polymorph lists. But you could store any polymorph marked object into that collection Default is false
  • nameProvider: specify the class of the name provider, you want to use for this entity. The name provider is being used to determine the name of the collection for this type. By Default it uses the DefaultNameProvider (which just uses the classname to build the collection name). see above


Marks POJOs for object mapping, but don't need to have an ID set. These objects will be marshalled and un-marshalled, but only as part of another object (Subdocument). This has to be set at class level.

You can switch off camel case conversion for this type and determine, whether data might be used polymorph.


ensures, that all write accesses to this entity are asynchronous.


switches OFF caching for this entity. This is useful if some superclass might have caches enabled and we need to disable it here.


Valid at: Class level

Tells Morphium to create a capped collection for this object (see capped collections above).


  • maxSize: maximum size in byte. Is used when converting to a capped collection
  • maxNumber: number of entries for this capped collection


These are the collation settings for this given entity. will be used when creating new collections and indices


Special feature for Morphium: this annotation has to be added for at lease one field of type Map<String,Object>. It does make sure, that all data in Mongo, that cannot be mapped to a field of this entity, will be added to the annotated Map properties.

by default this map is read only. But if you want to change those values or add new ones to it, you can set readOnly=false.


It's possible to define aliases for field names with this annotation (hence it has to be added to a field).

List<String> strLst;

in this case, when reading an object from MongoDB, the name of the field strLst might also be stringList or string_list in mongo. When storing it, it will always be stored as strLst or str_lst according to configs camelcase settings.

This feature comes in handy when migrating data.


has to be added to both the class and the field(s) to store the creation time in. This value is set in the moment, the object is being stored to mongo. The data type for creation time might be:

  • long / Long: store as timestamp
  • Date: store as date object
  • String: store as a string, you may need to specify the format for that


same as creation time, but storing the last access to this type. Attention: will cause all objects read to be updated and written again with a changed timestamp.

Usage: find out, which entries on a translation table are not used for quite some time. Either the translation is not necessary anymore or the corresponding page is not being used.


Same as the two above, except the timestamp of the last change (to mongo) is being stored. The value will be set, just before the object is written to mongo.


Define the read preference level for an entity. This annotation has to be used at class level. Valid types are:

  • PRIMARY: only read from primary node
  • PRIMARY_PREFERED: if possible, use primary.
  • SECONDARY: only read from secondary node
  • SECONDARY_PREFERED: if possible, use secondary
  • NEAREST: I don't care, take the fastest


Very important annotation to a field of every entity. It marks that field to be the id and identify any object. It will be stored as _id in mongo (and will get an index).

The Id may be of any type, though usage of ObjectId is strongly recommended.


Define indexes. Indexes can be defined for a single field. Combined indexes need to be defined on class level. See above.


List of fields in class, that can be ignored. Defaults no none.

usually an exact match, but can use ~ as substring, / as regex marker

Field names are JAVA Fields, not translated ones for mongo

IgnoreFields will not be honored for fields marked with @Property and a custom fieldname

this will be inherited by subclasses!

@IgnoreFields({"var1", "var3"})
public class TestClass {
public MorphiumId id;
public int var1;
public int var2;
public int var3;


this is a positive list of fields to use for MongoDB. All fields, not listed here will be ignored when it comes to mongodb.

public class TestClass2 {
public MorphiumId id;
public int var1;
public int var2;
public int var3;

LimitToFields also takes a Class as an argument, then the fields will be limited to the fields of the given class.

@LimitToFields(type = TestClass2.class)
public class TestClass3 extends TestClass2 {

public String notValid;


Can be added to any field. This not only has documenting character, it also gives the opportunity to change the name of this field by setting the fieldName value. By Default the fieldName is ".", which means "fieldName based".


Mark an entity to be read only. You'll get an exception when trying to store.


Mark a field to keep the current Version number. Field needs to be of type Long!


If you have a member variable, that is a POJO and not a simple value, you can store it as reference to a different collection, if the POJO is an Entity (and only if!).

This also works for lists and Maps. Attention: when reading Objects from disk, references will be de-referenced, which will result into one call to mongo each.

Unless you set lazyLoading to true, in that case, the child documents will only be loaded when accessed.

Lazy Loaded references

Morphium supports lazy loading of references. This is easy to use, just add @Reference(lazyLoading=true) to the reference you want to have them loaded lazyly.

public class MyEntity {
private UncachedObject myReference;  //will be loaded when first accessed
private MyEntity ent; //will be loaded when this object is loaded - use with caution
//this could cause an endless loop
private MyEntity embedded; //this object is not available on its own
//its embedded as subobject in this one

When a reference is being lazy loaded, the corresponding field will be set with a Proxy for an instance of the correct type, where only the ObjectID is set. Any access to it will be catched by the proxy, and any method will cause the object to be read from DB and deserialized. Hence this object will only be loaded upon first access.

It should be noted that when using Object.toString(); for testing that the object will be loaded from the database and appear to not be lazy loaded. In order to test Lazy Loading you should load the base object with the lazy reference and access it directly and it will be null. Additionally the referenced object will be null until the references objects fields are accessed.


Do not store the field - similar to @IgnoreFields or @LimitToFields


Cache settings for this entity, see the chapter about transparent caching above for more details.


Encryption settings for this field. See chapter about field encryption for details


Usually, Morphium does not store null values. That means, the corresponding document just would not contain the given field(s) at all.

Sometimes that might cause problems, so if you add @UseIfNull to any field, it will be stored into mongo even if it is null.


this annotation for an Entity tells morphium, that this entity does have some lifecycle methods defined. Those methods all need to be marked with the corresponding annotation:

  • @PostLoad
  • @PostRemove
  • @PostStore
  • @PostUpdate
  • @PreRemove - may throw a MorphiumAccessVetoException to abort the removal
  • @PreStore - may throw a MorphiumAccessVetoException to abort store
  • @PreUpdate - may throw a MorphiumAccessVetoException to abort update

the methods where those annotations are added must not have any parameters. They should only access the local object/entity.


only used auto-versioning is enabled in @Entity. Defines the field to hold the version number.


Specify the safety for this entity when it comes to writing to mongo. This can range from "NONE" to "WAIT FOR ALL SLAVES". Here are the available settings:

  • timeout: set a timeout in ms for the operation - if set to 0, unlimited (default). If set to negative value, wait relative to replication lag
  • level: set the safety level:
    • IGNORE_ERRORS None, no checking is done
    • NORMAL None, network socket errors raised
    • BASIC Checks server for errors as well as network socket errors raised
    • WAIT_FOR_SLAVE Checks servers (at lease 2) for errors as well as network socket errors raised
    • MAJORITY Wait for at least 50% of the slaves to have written the data
    • WAIT_FOR_ALL_SLAVES: waits for all slaves to have committed the data. This is depending on how many slaves are available in replica set. Wise timeout settings are important here. See WriteConcern in MongoDB Java-Driver for additional information

Cluster awareness

Morphium is tracking the cluster status internally in order to react properly on different scenarios6. For example, if one node goes down, waiting for all nodes to write the data will result in the application blocking until the last cluster member came back up again.

This is defined by the w-Setting in WriteSafety. In a nutshell, it tells mongo on how many cluster nodes you want to have written, and will wait until this number is reached.

This caused major problems with our environments, like having different cluster configurations in test and production environments.

Morphium fixes that issue in that way, that when "WAIT_FOR_ALL_SLAVES" is defined in WriteSafety, it will set the w-value according to the number of available slaves, resulting in no blocking. 7

Annotation Inheritance

By default, Java does not support the inheritance of annotations. This is ok in most cases, but in the case of entities it's a bugger. We added inheritance to Morphium to be able to build flexible data structures and store them to mongo.


Well, it's quite easy, actually ;-) The algorithm for getting the inherited annotations looks as follows (simplified)

  1. Take the annotations from the current class, if found, return it
  2. Take the superclass, if superclass is "Object" return null
  3. if there is the annotation to look for, return it
  4. continue with step 1

This way, all annotations in the hierarchy are taken into account and the most recent one is taken. You can always change the annotations when subclassing, although you cannot "erase" them (which means, if you inherit from an entity, it's always an entity). For Example:

public class Person {
private ObjectId id;

And the subclass:

   @Cache(writeCache=true, readCache=false)
public class Parent {
private List<Person> parentFrom;

Please keep in mind, that unless specified otherwise, the classname will be taken as the name for your collection. Also, be sure to store your classname in the collection (set polymorph=true in @Entity annotation) if you want to store them in one collection.

Changestream support

MongoDB introduced a feature called changestreams with V4.0 of mongodb. This is a special search that returns all changes to a database or collection. This is very useful if you want to be notified about changes to certain types or about certain commands being run.

Changestreams are only available when connected to a replicaset.

Morphium does support changestreams, in fact the messaging subsystem is built completely relying on this feature.

The easiest way to use changestreams is to use Morphiums ChangeStreamMonitor:

ChangeStreamMonitor m = new ChangeStreamMonitor(morphium, UncachedObject.class);
final AtomicInteger cnt = new AtomicInteger(0);

m.addListener(evt -> {
cnt.set(cnt.get() + 1);
return true;
for (int i = 0; i < 100; i++) { UncachedObject("value " + i, i));
assert (cnt.get() >= 100 && cnt.get() <= 101) : "count is wrong: " + cnt.get(); UncachedObject("killing", 0));

The monitor by definition runs asynchronous, it uses the watch methods to database or collection.

  • type, boolean updateFullDocument,ChangeStreamListener lst): this watches in a synchronous call for any change event. This call blocks! until the Listener returns false
  • morphium.watchAsync(...) (same parameters as above), runs asynchronously. attention: the Settings for asyncExcecutor in MorphiumConfig might affect the behaviour of this call.

There are also methods for watching all changes, that happen in the connected database. This might result in a lot of callbacks: watchDB() and watchDBAsync().


there is also an older implementation of this, the OplogMonitor. This one does more or less the same thing as the ChangeStreamMonitor, but also runs with older installations of MongoDB (when connected to a ReplicaSet).

You'd probably want to use the ChangestreamListener instead, as it is more efficient.

OplogListener lst = data -> {;
gotIt = true;
OplogMonitor olm = new OplogMonitor(morphium);

UncachedObject u = new UncachedObject("test", 123);;

assert (gotIt);
gotIt = false;

morphium.set(u, UncachedObject.Fields.value, "new value");
assert (gotIt);
gotIt = false;

u = new UncachedObject("test", 123);;
assert (!gotIt);


partial updating

The idea behind partial updates is, that only the changes to an entity are transmitted to the database and will thus reduce the load on network and MongoDB itself.

This is the easiest way - you already know, what fields you changed and maybe you even do not want to store fields, that you actually did change. In that case, call the updateUsingFields-Method:

   UncachedObject o....
o.setValue("A value");
//does only send updates for Value to mongodb
//counter is ignored

updateUsingFields() honours the lifecycle methods as well as caches (write cache or clear read_cache on write). take a look at some code from the corresponding JUnit test for better understanding:

UncachedObject o... //read from MongoDB
morphium.updateUsingFields(o, "value");"uncached object altered... look for it");
Query<UncachedObject> c=morphium.createQueryFor(UncachedObject.class);
UncachedObject fnd= (UncachedObject) c.f("_id").eq( o.getMongoId()).get();
assert(fnd.getValue().equals("Updated!")):"Value not changed? "+fnd.getValue();

BulkRequest support

If you need to send a lot of write requests to MongoDB, it might be useful to use bulk requests for that. MongoDB does have support for that. It means, that not each command is sent on its own, but all are sent in one single bulk command to the database, which is a lot more efficient.

To use that via Morphium you need to add your requests to the BulkRequestContext:

MorphiumBulkContext c = morphium.createBulkRequestContext(UncachedObject.class, false);
c.addSetRequest(morphium.createQueryFor(UncachedObject.class).f("counter").gte(0), "counter", 999, true, true);
//could add more requests here
Map<String, Object> ret = c.runBulk();

There are all basic operations you might send in a bulk:

  • insert
  • delete
  • set/unset
  • inc/dec
  • update
  • mul (multiplication)
  • ...

If there is a special request, where there is no direct support in bulk context, use the generic method addCustomUpdateRequest() for adding a request. You need to pass on your requests Map-Representation.

Transaction support

MongoDB does have support for transactions in newer releases. Morphium does support that as well:

public void transactionTest() throws Exception {
for (int i = 0; i < 10; i++) {
try {
TestEntityNameProvider.number.incrementAndGet();"Entityname number: " + TestEntityNameProvider.number.get());

Thread.sleep(100);"Count after transaction start: " + morphium.createQueryFor(UncachedObject.class).countAll());
UncachedObject u = new UncachedObject("test", 101);;
long cnt = morphium.createQueryFor(UncachedObject.class).countAll();
if (cnt != 11) {
assert (cnt == 11) : "Count during transaction: " + cnt;
}, "counter", 1);
u = morphium.reread(u);
assert (u.getCounter() == 102);
cnt = morphium.createQueryFor(UncachedObject.class).countAll();
u = morphium.reread(u);
assert (u == null);
assert (cnt == 10) : "Count after rollback: " + cnt;
} catch (Exception e) {
log.error("ERROR", e);


Internally, Morphium uses the transaction context if this thread started a transaction (if you need a transaction spanning over Threads, you need to pass on the current transaction session:

//other thread

Caveat: mongoDB does not support nested transactions (yet), so you will get an Exception when trying to start another transaction in the same thread.

Listeners in Morphium

there are a lot of listeners in Morphium that help you get informed about what is going on in the system. Some of which also might help you, to adapt behaviour according to your needs:


Morphium is monitoring the status of the replicaset it is connected to (default is every 5s, but can be changed in MorphiumConfigs setting replicaSetMonitoringTimeout). You can get this information on demand, by calling morphium.getReplicasetStatus().

But you can also be informed whenever there is a change in the cluster by implementing the interface (since Morphium V4.2):

public interface ReplicasetStatusListener {

void gotNewStatus(Morphium morphium, ReplicaSetStatus status);

* infoms, if replicaset status could not be optained.
* @param numErrors - how many errors getting the status in a row we already havei
void onGetStatusFailure(Morphium morphium, int numErrors);

* called, if the ReplicasetMonitor aborts due to too many errors
* @param numErrors - number of errors occured
void onMonitorAbort(Morphium morphium, int numErrors);

* @param hostsDown - list of hostnamed not up
* @param currentHostSeed - list of currently available replicaset members
void onHostDown(Morphium morphium, List<String> hostsDown,List<String> currentHostSeed);

The ReplicasetStatus does contain a lot of information about the replicaset itself:

public class ReplicaSetStatus {
private String set;
private String myState;
private String syncSourceHost;
private Date date;
private int term;
private int syncSourceId;
private long heartbeatIntervalMillis;
private int majorityVoteCount;
private int writeMajorityCount;
private int votingMembersCount;
private int writableVotingMembersCount;
private long lastStableRecoveryTimestamp;
private List<ReplicaSetNode> members;
private Map<String,Object> optimes;
private Map<String,Object> electionCandidateMetrics;

public class ReplicaSetNode {
private int id;
private String name;
private double health;
private int state;
@Property(fieldName = "stateStr")
private String stateStr;
private long uptime;
@Property(fieldName = "optimeDate")
private Date optimeDate;

@Property(fieldName = "lastHeartbeat")
private Date lastHeartbeat;
private int pingMs;
private String syncSourceHost;
private int syncSourceId;
private String infoMessage;
private Date electionDate;
private int configVersion;
private int configTerm;
private String lastHeartbeatMessage;
private boolean self;

See mongoDB documentation of rs.status() command for more information on the different fields.


Via this interface, you will be informed about cache operations and may interfere with them or change the behaviour:

public interface CacheListener {
* ability to alter cached entries or avoid caching overall
* @param toCache - datastructure containing cache key and result
* @param <T>     - the type
* @return false, if not to cache
//return the cache entry to be stored, null if not
<T> CacheEntry<T> wouldAddToCache(Object k, CacheEntry<T> toCache, boolean updated);

//return false, if you do not want cache to be cleared
<T> boolean wouldClearCache(Class<T> affectedEntityType);

//return false, if you do not want entry to be removed from cache
<T> boolean wouldRemoveEntryFromCache(Object key, CacheEntry<T> toRemove, boolean expired);



This are special cache listeners which will be informed, when a cache needs to be updated because of incoming clear or update requests. There are two direct sub-interfaces:

  • WatchingCacheSyncListener: to be used with WatchingCacheSynchronizer
  • MessagingCacheSyncListener: to be used with MessagingCacheSynchronizer

The base interface is CacheSyncListener:

public interface CacheSyncListener {
* before clearing cache - if cls == null whole cache
* Message m contains information about reason and stuff...
void preClear(Class cls) throws CacheSyncVetoException;

void postClear(Class cls);

and the subclasses WatchingCacheSyncListener (just adds one other method):

public interface WatchingCacheSyncListener extends CacheSyncListener {
void preClear(Class<?> type, String operation);


and the MessagingCacheSyncListener which adds some Messaging based methods:

public interface MessagingCacheSyncListener extends CacheSyncListener {

* Class is null for CLEAR ALL
* @param cls
* @param m   - message about to be send - add info if necessary!
* @throws CacheSyncVetoException
void preSendClearMsg(Class cls, Msg m) throws CacheSyncVetoException;

void postSendClearMsg(Class cls, Msg m);


As already mentioned, this listener is used to be informed about changes in your data.

public interface ChangeStreamListener {
* return true, if you want to continue getting events.
* @param evt
* @return
boolean incomingData(ChangeStreamEvent evt);


This one is one of the core functionalities of Morphium messaging, this is the placed to be informed about incoming messages:

public interface ChangeStreamListener {
* return true, if you want to continue getting events.
* @param evt
* @return
boolean incomingData(ChangeStreamEvent evt);


If you add a listener for these kind of events, you will be informed about any store via morphium. This is kind of the same thing as the LifeCycle annotation and the corresponding methods. But its a different design pattern. If a MorphiumAccessVetoException is thrown, the corresponding action is aborted.

public interface MorphiumStorageListener<T> {
void preStore(Morphium m, T r, boolean isNew) throws MorphiumAccessVetoException;

void preStore(Morphium m, Map<T, Boolean> isNew) throws MorphiumAccessVetoException;

void postStore(Morphium m, T r, boolean isNew);

void postStore(Morphium m, Map<T, Boolean> isNew);

void preRemove(Morphium m, Query<T> q) throws MorphiumAccessVetoException;

@SuppressWarnings({"EmptyMethod", "UnusedParameters"})
void preRemove(Morphium m, T r) throws MorphiumAccessVetoException;

void postRemove(Morphium m, T r);

void postRemove(Morphium m, List<T> lst);

void postDrop(Morphium m, Class<? extends T> cls);

void preDrop(Morphium m, Class<? extends T> cls) throws MorphiumAccessVetoException;

void postRemove(Morphium m, Query<T> q);

@SuppressWarnings({"EmptyMethod", "UnusedParameters"})
void postLoad(Morphium m, T o);

@SuppressWarnings({"EmptyMethod", "UnusedParameters"})
void postLoad(Morphium m, List<T> o);

void preUpdate(Morphium m, Class<? extends T> cls, Enum updateType) throws MorphiumAccessVetoException;

void postUpdate(Morphium m, Class<? extends T> cls, Enum updateType);

enum UpdateTypes {



there is a listener / watch functionality that works with older Mongodb installations. The OpLogListener is used by the OplogMonitor and uses the OpLog to inform about changes 8.

public interface OplogListener {
void incomingData(Map<String, Object> data);

Profiling Listener

If you need to gather performance data about your mongoDB setup, the Profiling listener has you covered. It gives detailed information about the duration of any write or read access:

public interface ProfilingListener {
void readAccess(Query query, long time, ReadAccessType t);

void writeAccess(Class type, Object o, long time, boolean isNew, WriteAccessType t);

The Aggregation Framework

The aggregation framework is a very powerful feature of MongoDB and Morphium supports it from the start9. But with Morphium V4.2.x we made use of it a lot easier.

Core of the aggregation Framework in Morphium is the Aggregator. This will be created (using the configured AggregatorFactory) by a Morphium instance.

Aggregator<Source,Result> aggregator=morphium.createAggregator(Source.class,Result.class);

This creates an aggregator that reads from the entity Source (or better the corresponding collection) and returns the results in Result. Usually you will have to define a Result entity in order to use aggregation, but with Morphium V4.2 it is possible to have a Map as a result class.

After preparing the aggregator, you need to define the stages. All currently available stages are also available in Morphium. For a list of available stages, just consult the mongodb documentation.

In a nutshell, the aggregation framework runs all documents through a pipeline of commands, that either reduce the input (like a query), change the output (a projection) or calculate some values (like with sum count etc).

The most important pipeline stage is probably the "group" stage. This is similar to the group by in SQL, but more powerful, as you can have several of those group stages in a pipeline.

here an Example with a simple pipeline:

Aggregator<UncachedObject, Aggregate> a = morphium.createAggregator(UncachedObject.class, Aggregate.class);
assert (a.getResultType() != null);
//reduce input
a = a.project("counter");
a = a.match(morphium.createQueryFor(UncachedObject.class)
//Sort, used with $first/$last
a = a.sort("counter");
//limit data
a = a.limit(15);
//group by - here we only have one static group, but could be any field or value
a ="all").avg("schnitt", "$counter").sum("summe", "$counter").sum("anz", 1).last("letzter", "$counter").first("erster", "$counter").end();

//result projection
HashMap<String, Object> projection = new HashMap<>();
projection.put("summe", 1);
projection.put("anzahl", "$anz");
projection.put("schnitt", 1);
projection.put("last", "$letzter");
projection.put("first", "$erster");
a = a.project(projection);

List<Aggregate> lst = a.aggregate();
assert (lst.size() == 1) : "Size wrong: " + lst.size();"Sum  : " + lst.get(0).getSumme());"Avg  : " + lst.get(0).getSchnitt());"Last :    " + lst.get(0).getLast());"First:   " + lst.get(0).getFirst());"count:  " + lst.get(0).getAnzahl());

assert (lst.get(0).getAnzahl() == 15) : "did not find 15, instead found: " + lst.get(0).getAnzahl();

But you could have that result grouped again for example or add fields to it or change values or ....

Consult the MongoDB documentation for more information about the aggregation pipeline.

Aggregation Expressions

MongoDB has support for an own expression language, that is mainly used in aggregation. _Morphium_s representation thereof is Expr.

Expr does have a lot of factory methods to create special Expr instances, for example Expr.string() returns a string expression (string constant), creates the "greater than" expression and so on.

Examples of expressions:

Expr e = Expr.add(Expr.field("the_field"), Expr.abs(Expr.field("test")), Expr.doubleExpr(128.0));
Object o = e.toQueryObject();
String val = Utils.toJsonString(o);;
assert(val.equals("{ \"$add\" :  [ \"$the_field\", { \"$abs\" :  [ \"$test\"] } , 128.0] } "));

e =, Expr.arrayExpr(Expr.intExpr(12), Expr.doubleExpr(1.2), Expr.field("testfield")));
assert(val.equals("{ \"$in\" :  [ 1.2,  [ 12, 1.2, \"$testfield\"]] } "));

e =, Expr.intExpr(14)), Expr.arrayExpr(Expr.intExpr(1), Expr.intExpr(14))), Expr.bool(true), Expr.field("test"));
assert(val.equals("{ \"$zip\" : { \"inputs\" :  [  [ 1, 14],  [ 1, 14]], \"useLongestLength\" : true, \"defaults\" : \"$test\" }  } "));

e = Expr.filter(Expr.arrayExpr(Expr.intExpr(1), Expr.intExpr(14), Expr.string("asV")), "str", Expr.string("NEN"));
assert(val.equals("{ \"$filter\" : { \"input\" :  [ 1, 14, \"asV\"], \"as\" : \"str\", \"cond\" : \"NEN\" }  } "));

the output of this little program would be:

{ "$add" :  [ "$the_field", { "$abs" :  [ "$test"] } , 128.0] } 
{ "$in" :  [ 1.2,  [ 12, 1.2, "$testfield"]] } 
{ "$zip" : { "inputs" :  [  [ 1, 14],  [ 1, 14]], "useLongestLength" : true, "defaults" : "$test" }  } 
{ "$filter" : { "input" :  [ 1, 14, "asV"], "as" : "str", "cond" : "NEN" }  } 

This way you can create complex aggregation pipelines:

     Aggregator<UncachedObject, Aggregate> a = morphium.createAggregator(UncachedObject.class, Aggregate.class);
assert (a.getResultType() != null);
a = a.project(Utils.getMap("counter", (Object) Expr.intExpr(1)).add("cnt2", Expr.field("counter")));
a = a.match("counter"), Expr.intExpr(100)));
a = a.sort("counter");
a = a.limit(15);
a ="schnitt", Expr.avg(Expr.field("counter"))).expr("summe", Expr.sum(Expr.field("counter"))).expr("anz", Expr.sum(Expr.intExpr(1))).expr("letzter", Expr.last(Expr.field("counter"))).expr("erster", Expr.first(Expr.field("counter"))).end();

This expression language can also be used in queries:

            Query<UncachedObject> q = morphium.createQueryFor(UncachedObject.class);
q.expr(, Expr.intExpr(50)));;
List<UncachedObject> lst = q.asList();
assert (lst.size() == 50) : "Size wrong: " + lst.size();

for (UncachedObject u : q.q().asList()) {
u.setDval(Math.random() * 100);;

q = q.q().expr(, Expr.field(UncachedObject.Fields.dval)));
lst = q.asList();

Hint: if you use Expr in your code, it is probably a good idea to use import static de.caluga.morphium.aggregation.Expr.*; to make the code easier to read and understand.

Additional information sources

There are some places, you also might want to look at for additional information on mongodb or Morphium:

Code Examples

Cache Synchronization

 Messaging msg = new Messaging(morphium, 100, true);
MessagingCacheSynchronizer cs = new MessagingCacheSynchronizer(msg, morphium);

Query<Msg> q = morphium.createQueryFor(Msg.class);
long cnt = q.countAll();
assert (cnt == 0) : "Already a message?!?! " + cnt;

cs.sendClearMessage(CachedObject.class, "test");
cnt = q.countAll();
assert (cnt == 1) : "there should be one msg, there are " + cnt;
public void nearTest() throws Exception {
ArrayList<Place> toStore = new ArrayList<Place>();
//        morphium.ensureIndicesFor(Place.class);
for (int i = 0; i < 1000; i++) {
Place p = new Place();
List<Double> pos = new ArrayList<Double>();
pos.add((Math.random() * 180) - 90);
pos.add((Math.random() * 180) - 90);
p.setName("P" + i);

Query<Place> q = morphium.createQueryFor(Place.class).f("position").near(0, 0, 10);
long cnt = q.countAll();"Found " + cnt + " places around 0,0 (10)");
List<Place> lst = q.asList();
for (Place p : lst) {"Position: " + p.getPosition().get(0) + " / " + p.getPosition().get(1));

@WriteSafety(level = SafetyLevel.MAJORITY)
public static class Place {
private ObjectId id;

public List<Double> position;
public String name;

public ObjectId getId() {
return id;

public void setId(ObjectId id) { = id;

public List<Double> getPosition() {
return position;

public void setPosition(List<Double> position) {
this.position = position;

public String getName() {
return name;

public void setName(String name) { = name;


public void basicIteratorTest() throws Exception {

Query<UncachedObject> qu = getUncachedObjectQuery();
long start = System.currentTimeMillis();
MorphiumIterator<UncachedObject> it = qu.asIterable(2);
assert (it.hasNext());
UncachedObject u =;
assert (u.getCounter() == 1);"Got one: " + u.getCounter() + "  / " + u.getValue());"Current Buffersize: " + it.getCurrentBufferSize());
assert (it.getCurrentBufferSize() == 2);

u =;
assert (u.getCounter() == 2);
u =;
assert (u.getCounter() == 3);
assert (it.getCount() == 1000);
assert (it.getCursor() == 3);

u =;
assert (u.getCounter() == 4);
u =;
assert (u.getCounter() == 5);

while (it.hasNext()) {
u =;"Object: " + u.getCounter());

assert (u.getCounter() == 1000);"Took " + (System.currentTimeMillis() - start) + " ms");

Asynchronous Read

public void asyncReadTest() throws Exception {
asyncCall = false;
Query<UncachedObject> q = morphium.createQueryFor(UncachedObject.class);
q = q.f("counter").lt(1000);
q.asList(new AsyncOperationCallback<UncachedObject>() {
public void onOperationSucceeded(AsyncOperationType type, Query<UncachedObject> q, long duration, List<UncachedObject> result, UncachedObject entity, Object... param) {"got read answer");
assert (result != null) : "Error";
assert (result.size() == 100) : "Error";
asyncCall = true;

public void onOperationError(AsyncOperationType type, Query<UncachedObject> q, long duration, String error, Throwable t, UncachedObject entity, Object... param) {
assert false;
int count = 0;
while (q.getNumberOfPendingRequests() > 0) {
assert (count < 10);
System.out.println("Still waiting...");
assert (asyncCall);

Asynchronous Write

public void asyncStoreTest() throws Exception {
asyncCall = false;
waitForWrites();"Uncached object preparation");
Query<UncachedObject> uc = morphium.createQueryFor(UncachedObject.class);
uc = uc.f("counter").lt(100);
morphium.delete(uc, new AsyncOperationCallback<Query<UncachedObject>>() {
public void onOperationSucceeded(AsyncOperationType type, Query<Query<UncachedObject>> q, long duration, List<Query<UncachedObject>> result, Query<UncachedObject> entity, Object... param) {"Objects deleted");

public void onOperationError(AsyncOperationType type, Query<Query<UncachedObject>> q, long duration, String error, Throwable t, Query<UncachedObject> entity, Object... param) {
assert false;

uc = uc.q();
uc.f("counter").mod(3, 2);
morphium.set(uc, "counter", 0, false, true, new AsyncOperationCallback<UncachedObject>() {
public void onOperationSucceeded(AsyncOperationType type, Query<UncachedObject> q, long duration, List<UncachedObject> result, UncachedObject entity, Object... param) {"Objects updated");
asyncCall = true;


public void onOperationError(AsyncOperationType type, Query<UncachedObject> q, long duration, String error, Throwable t, UncachedObject entity, Object... param) {"Objects update error");


assert(morphium.createQueryFor(UncachedObject.class).f("counter").eq(0).countAll() > 0);
assert (asyncCall);


This document was written by the authors with most care, but there is no guarantee for 100% accuracy. If you have any questions, find a mistake or have suggestions for improvements, please contact the authors of this document and the developers of morphium via or send an email to

  1. you can even use aggregation on it, to gather more information about your messages ↩︎

  2. those throw an Exception to let you know, it is missing ↩︎

  3. does only make sense, when there is more than one recipient usually ↩︎

  4. attention: the "top level" document needs to be an Entity to have all necessary settings there. But "subdocuments"/properties might be just serializable ↩︎

  5. text search and text indices can be disabled in mongoDB config. When creating the index, it would throw an Exception ↩︎

  6. can be switched off in morphiumConfig ↩︎

  7. as it takes some time for Morphium and mongo do determine if a cluster member is down, some requests might actually block ↩︎

  8. also only works when connected to a replicaset ↩︎

  9. does not work with the `InMemoryDriver' yet ↩︎

  10. this blog is powered by Morphium and mongodb ↩︎

category: global

New Release of Morphium V2.2.6

2014-09-02 - Tags:

sorry, no english version available

category: Computer

New release V2.2.4 of #morphium - the #MongoDB POJO #mapper

2014-08-20 - Tags:

sorry, no english version available

category: Computer

New Morphium Release V2.2.3

2014-08-08 - Tags:

sorry, no english version available

category: Java --> programming --> Computer

Neues Release von #Morphium V2.1.1 - DER #MongoDB POJO Mapper

2014-04-16 - Tags:

sorry, no english version available

category: global

New Release of #Morphium V2.1.1 - THE #MongoDB POJO Mapper

2014-04-16 - Tags:

sorry, no english version available

category: global

Major #Morphium Release V2.1.0 for #MongoDb 2.6

2014-04-09 - Tags:

sorry, no english version available

category: Java --> programming --> Computer

Morphium V2.0.27 #mogodb Object mapper

2014-04-01 - Tags:

sorry, no english version available

category: Computer --> programming --> Java

Anderes Character Encoding JDK7u45 vs Jdk7u4

2013-11-27 - Tags: java programming

no english version available yet

category: Java --> programming --> Computer

Neue Version von Morphium V2.0.24

2013-11-20 - Tags:

sorry, no english version available

category: Computer

MongoDB auf dedizierter Hardware

2013-11-15 - Tags:

sorry, no english version available

category: Java --> programming --> Computer

New Docu for Morphium V2.0.23

2013-11-07 - Tags:

sorry, no english version available

category: Computer

using Qnap as GIT-Server - SSH Problems

2013-11-06 - Tags: git qnap

Linux really rules, especially if you consider the possibilities you get, adding functionalities to linux based gadgets or fix / add missing functionalities.

The latter one is something that also can be said about the Qnap storage system. I use git for my own little software projects. This is cool as a version control system and easy to use - better than SVN or CVS.

So I have my repositories on a share on my qnap. You can mount the share and use git to synchronize stuff, that works fine so fare. But causes problems, when working remotely where you do not want to mount the share in order to be able to push things.

Luckily you can install additional tools on the qnap, git is one of them. And it works fine out of the box, you can use ssh to access git and the repositories.

Unfortunately is the sshd that comes with the qnap somewhat works strange, you can alter the /etc/sshd to what you want, it won't be possible to log in as something else than admin.

I do not want to open some root login to this qnap, no way. So I digged a bit deeper and found out, that the sshd is altered to only allow logins as admin.

But I am root on this machine, so lets hack.

  1. install openssh via the Optware installation frontend. unfortunately this alone does not work, as the installation is not replacing the existing one. So we need to go further
  2. rename original in /usr/sbin: mv sshd sshd.qnap
  3. create link: ln -s /opt/sbin/sshd
  4. alter your sshd_config to your needs
  5. restart sshd (either via the GUI by disabling and re-enabling remote login or via kill the SSHD - Attention, this might and often will kick you out)

ok, now you should be able to log in as someone else than admin.

now you only need to create your repositories to your liking. I created one special share for it (which can also be mounted).

in my case it would be something like: git clone user@qnap:/share/development/git/repo

if you think this might be a security risk, you could set the login shell for that user to git-shell to avoid direkt access of this user.

Happy Hacking

category: Java --> programming --> Computer

New Version of Morphium Mongodb POJO Mapper V2.0.23

2013-11-06 - Tags:

sorry, no english version available

category: Computer --> programming --> Objective-C

RSA Implementierung in Objective-C

2013-11-04 - Tags: objective-c security encryption

Still work in progress, does not 100% reflect the German Version

I already wrote about my effort in creating a completely independent RSA implementation here. I posted it also on github-RSALib, so maybe somebody wants to use it... or wants to give feedback.

There is also a byte-compatible java version here. Thing is, it is not very easy to use encryption on different operating systems, e.g. iOS and Linux, and assume things are working fine. Actually, usually it is quite the opposite. But the most important Reason for those implementations was to have an insight in how encryption can be done without the os, so to exactly know what happens. No OS-backdoor could break into that... at least, not as easily.

The proof of concept is a very complicated Messaging System, that runs on iOS using the RSAImplementation mentioned above, connecting to a server implemented in java using the same implementation in java. Works quite fine...

what now

I was thinking about creating something useful with that code. I used to write a private diary on my mac using the App DayOne. This is a very cool app, runs on iOS and OSX, synchronizes you entries between different installations and is very beautiful to use - and is not encrypted at all! For a diary App! How can that be? Even the simplest physical diary usually has some kind of lock on it or somathing... But with DayOne all entries are store in plain text on the disk drive of all machines you use DayOne and synchronize it.

So, if your MacBook is lost - all your private Thoughts are plain to see there... Congratulations.

There is actually no good encrypted Diary in the App Store. Some encrypt good, but are very uncomfortable. Some are beautiful, but do not encrypt good... And so on...

So... a new Diary App was needed (at least for me):

For my Eyes Only

This is the name of the App, and this is the name the app will be published with. The Idea is quite easy, use the AES/RSA Implementations above to create a securely encrypted Diary... Great Idea!

What features do we need:

  • RSA Encryption. But where to put the Key? Obviously not a good idea to store the key in the app itself, but how about storing it hidden in a picture (steganography)? or on an usb stick? Both options should be possible
  • The keys are of course AES encrypted
  • It should be able to be synchronized using iCloud (maybe dropbox)
  • It should be possible to add photos and such to your entries, but those need to be encrypted as well
  • you need to be able to search for things

Problem is: using CoreData is not working, as the structure would still be visible, and searching would not work any way, as everything would be encrypted...

So... We need to create our own storage system.

Implementation Diary

I will put in some things I did during the implementation here, even if the app will never be published, it might be worth having this info around somehow...

category: Apple --> Computer

von Java zu Objective-C

2013-10-24 - Tags:

sorry, no english version available

category: Java --> programming --> Computer

Morphium: Minor feature release avaliable V2.0.22

2013-10-16 - Tags: java-2 morphium-2 programming

sorry, no english version available

category: global

Morphium - MongoDb Object Mapper now on GitHub

2013-10-15 - Tags:

sorry, no english version available

category: Computer --> programming --> Objective-C

Verschl├╝sselung auf iOS mit Objective-C

2013-10-02 - Tags: ios objective-c-2 rsa

no english version available yet

Im Zuge der ganzen NSA/Prism/Snowden Diskussion und auch weil es mich interessiert, hab ich mich mal rangesetzt und mir ein paar Gedanken zum Thema "privacy & Security" im Internet gemacht.

Was momentan fehlt ist ein Weg, wirklich sicher zu kommunizieren. Die DE-Mail ist ja nur ein besserer Witz (sicherheitstechnisch zumindest) und GPG / PGP ist viel zu kompliziert zu benutzen und vor allem - man ben├Âtigt eine Identit├Ątspr├╝fung, "komische" Zertifikate und anderes, was der Otto-Normal-User so nicht wirklich versteht.

Das ist alles viel zu weit davon entfernt, wirklich brauchbar zu sein oder auch nur ann├Ąhernd Email den Rang abzulaufen. Eigentlich m├╝sste es vollkommen transparent laufen und alles Verschl├╝sselt werden - von End-To-End. D.h. von einem Client werden die Daten so verschl├╝sselt, dass nur der Empf├Ąnger diese Daten entschl├╝sseln kann.

Im Zuge dessen habe ich versucht, auf IOS eine Public-Key-Verschl├╝sselung hin zu bekommen, um zu sehen, was man damit noch so anstellen kann. Das ganze hat mich doch vor mehr Probleme gestellt, als gedacht.

Zun├Ąchst mal ein Symmetrisches Verschl├╝sselungsverfahren - auch wenn das f├╝r das Ziel einer End-To-End-Verschl├╝sselung nicht wirklich ausreichend ist (symmetrische Schl├╝ssel kann man immer per Brute-Force angreifen), so ist es doch wichtig, dass die Keys nicht irgendwo unverschl├╝sselt rumliegen, sondern, wenn ├╝berhaupt, dann mit einem guten Passwort verschl├╝sselt werden.

AES in Objective-C

Das war erstaunlicherweise gar nicht so kompliziert, es gibt da wirklich eine Menge Beispiele im Netzt zu, die auch wirklich funktionieren. Hier der Code zum Verschl├╝sseln von NSData:

[codesyntax lang="objc"]

- (NSData *)AES256EncryptWithKey:(NSString *)key{
    // 'key' should be 32 bytes for AES256, will be null-padded otherwise
    char keyPtr[kCCKeySizeAES256 + 1]; // room for terminator (unused)
    bzero( keyPtr, sizeof( keyPtr ) ); // fill with zeroes (for padding)

    // fetch key data
    [key getCString:keyPtr maxLength:sizeof( keyPtr ) encoding:NSUTF8StringEncoding];

    NSUInteger dataLength = [self length];

    //See the doc: For block ciphers, the output size will always be less than or
    //equal to the input size plus the size of one block.
    //That's why we need to add the size of one block here
    size_t bufferSize = dataLength + kCCBlockSizeAES128;
    void *buffer = malloc( bufferSize );

    size_t numBytesEncrypted = 0;
    CCCryptorStatus cryptStatus = CCCrypt( kCCEncrypt, kCCAlgorithmAES128, kCCOptionPKCS7Padding,
                                          keyPtr, kCCKeySizeAES256,
                                          NULL /* initialization vector (optional) */,
                                          [self bytes], dataLength, /* input */
                                          buffer, bufferSize, /* output */
                                          &numBytesEncrypted );
    if( cryptStatus == kCCSuccess )
        //the returned NSData takes ownership of the buffer and will free it on deallocation
        return [NSData dataWithBytesNoCopy:buffer length:numBytesEncrypted];

    free( buffer ); //free the buffer
    return nil;


Das funktioniert tadellos und ist einfach zu verwenden. Da man symmetrisch verschl├╝sselt, muss man auch nicht mit 2 Keys rumhantieren und die evtl. sogar im KeyStore ablegen.

Die Entschl├╝sselung ist auch recht simpel:

[codesyntax lang="objc"]

- (NSData *)AES256DecryptWithKey:(NSString *)key{

    // 'key' should be 32 bytes for AES256, will be null-padded otherwise
    char keyPtr[kCCKeySizeAES256+1]; // room for terminator (unused)
    bzero( keyPtr, sizeof( keyPtr ) ); // fill with zeroes (for padding)

    // fetch key data
    [key getCString:keyPtr maxLength:sizeof( keyPtr ) encoding:NSUTF8StringEncoding];

    NSUInteger dataLength = [self length];

    //See the doc: For block ciphers, the output size will always be less than or
    //equal to the input size plus the size of one block.
    //That's why we need to add the size of one block here
    size_t bufferSize = dataLength + kCCBlockSizeAES128;
    void *buffer = malloc( bufferSize );

    size_t numBytesDecrypted = 0;
    CCCryptorStatus cryptStatus = CCCrypt( kCCDecrypt, kCCAlgorithmAES128, kCCOptionPKCS7Padding,
                                          keyPtr, kCCKeySizeAES256,
                                          NULL /* initialization vector (optional) */,
                                          [self bytes], dataLength, /* input */
                                          buffer, bufferSize, /* output */
                                          &numBytesDecrypted );

    if( cryptStatus == kCCSuccess )
        NSLog(@"Decrypt success");
        //the returned NSData takes ownership of the buffer and will free it on deallocation
        return [NSData dataWithBytesNoCopy:buffer length:numBytesDecrypted];

    free( buffer ); //free the buffer
    return nil;


RSA Implementierung

Standardweg mit Bordmitteln

Zun├Ąchst hab ich mal die Bordmittel von iOS 6 (und sp├Ąter 7) benutzt, um daten Verschl├╝sseln zu k├Ânnen. Das hat zun├Ąchst auch recht gut funktioniert - hier ein code-Beispiel:

[codesyntax lang="objc" lines="fancy" lines_start="1"]

NSData *wrappedSymmetricKey = data;
SecKeyRef key = yes ? self.publicKeyRef : self.privateKeyRef;

size_t cipherBufferSize = SecKeyGetBlockSize(key);
size_t keyBufferSize = [wrappedSymmetricKey length];

NSMutableData *bits = [NSMutableData dataWithLength:keyBufferSize];
OSStatus sanityCheck = SecKeyDecrypt(key,kSecPaddingPKCS1,
(const uint8_t *) [wrappedSymmetricKey bytes],cipherBufferSize,[bits mutableBytes],&keyBufferSize);
NSAssert(sanityCheck == noErr, @"Error decrypting, OSStatus == %ld.", sanityCheck);

[bits setLength:keyBufferSize];

return bits;



Das ist der Code, der einen gegebenen Datenblock (NSData*) verschl├╝sselt und die verschl├╝sselten Daten wiederum als NSData zur├╝ckgibt. (die Basis f├╝r diesen Code findet sich hier). Das funktioniert so weit auch wunderbar.... solange man bei jedem Start der Application, das Schl├╝sselpaar neu generiert.

Aber eins nach dem anderen. Was macht der Code? Er nutzt die auch unter OSX bekannte "Schl├╝sselbundverwaltung" bzw. dessen Pendant von iOS. Dort m├╝ssen die Schl├╝sselpaare abgelegt werden um sie entsprechend nutzen zu k├Ânnen.

Die Erstellung und speicherung eines Schl├╝sselpaares geht z.B. so (auch wieder von hier):

[codesyntax lang="objc"]

OSStatus sanityCheck = noErr;
    publicKeyRef = NULL;
    privateKeyRef = NULL;

    // First delete current keys.
    [self deleteAsymmetricKeys];

    // Container dictionaries.
    NSMutableDictionary *privateKeyAttr = [NSMutableDictionary dictionaryWithCapacity:0];
    NSMutableDictionary *publicKeyAttr = [NSMutableDictionary dictionaryWithCapacity:0];
    NSMutableDictionary *keyPairAttr = [NSMutableDictionary dictionaryWithCapacity:0];

    // Set top level dictionary for the keypair.
    [keyPairAttr setObject:(__bridge id) kSecAttrKeyTypeRSA forKey:(__bridge id) kSecAttrKeyType];
    [keyPairAttr setObject:[NSNumber numberWithUnsignedInteger:kSecAttrKeySizeInBitsLength] forKey:(__bridge id) kSecAttrKeySizeInBits];

    // Set the private key dictionary.
    [privateKeyAttr setObject:[NSNumber numberWithBool:YES] forKey:(__bridge id) kSecAttrIsPermanent];
    [privateKeyAttr setObject:privateTag forKey:(__bridge id) kSecAttrApplicationTag];
    // See SecKey.h to set other flag values.

    // Set the public key dictionary.
    [publicKeyAttr setObject:[NSNumber numberWithBool:YES] forKey:(__bridge id) kSecAttrIsPermanent];
    [publicKeyAttr setObject:publicTag forKey:(__bridge id) kSecAttrApplicationTag];
    // See SecKey.h to set other flag values.

    // Set attributes to top level dictionary.
    [keyPairAttr setObject:privateKeyAttr forKey:(__bridge id) kSecPrivateKeyAttrs];
    [keyPairAttr setObject:publicKeyAttr forKey:(__bridge id) kSecPublicKeyAttrs];

    // SecKeyGeneratePair returns the SecKeyRefs just for educational purposes.
    sanityCheck = SecKeyGeneratePair((__bridge CFDictionaryRef) keyPairAttr, &publicKeyRef, &privateKeyRef);

    if (publicKeyRef == NULL) {
        NSLog(@"did not get keys");
    LOGGING_FACILITY( sanityCheck == noErr && publicKeyRef != NULL && privateKeyRef != NULL, @"Something really bad went wrong with generating the key pair." );



Dabei wird ein neues Schl├╝sselpaar generiert und die Referenzen darauf lokal abgelegt (Variablen publicKeyRef und privateKeyRef).

So weit so gut, das klappt wunderbar, hat nur einige Einschr├Ąnkungen: so kann man keine Schl├╝ssel erzeugen, die gr├Â├čer sind als 4096 bit, au├čerdem m├╝ssen an stelle von dem Schl├╝ssel selbst ja auch noch einige Zusatzinformationen abgespeichert werden, wie z.B. unter welchem "Namen" die keys abgelegt werden etc.

Das, was mich am meisten daran gest├Ârt hat, ist aber, dass es eine sehr unhandliche Schnittstelle ist, wo zwischen Objective-C und C/C++ hin und hergemapped werden muss. (Anm. ich hab die letzten Jahr(zehnt)e haupts├Ąchlich Java programmiert, da ist alles etwas "sauberer"). Eine sch├Ânere Objective-C implementierung hab ich nicht gefunden. (falls ihr eine kennt, immer her damit...)

Aber was mich wirklich genervt hat, ist, dass ich es ums verrecken nicht hin bekommen habe, diese d├Ąmlichen RSA-Keys z.B. aus den UserDefaults wieder zu lesen. No chance... man kann sich die Bits zwar holen, kann aus den Bits auch wieder Keys erstellen lassen... allerdings landen die dann irgendwo im Storage und ich bekomme den Key nicht mehr da raus. Entweder er ist nil oder er kann nicht zum entschl├╝sseln benutzt werden.

Also, hier der letzte Code-Stand, evtl. habt ihr ja ne Idee, was da klemmen kann:

[codesyntax lang="objc"]

 NSUserDefaults *defaults = [NSUserDefaults standardUserDefaults];
    NSData *privKey = [defaults dataForKey:@"privateKey"];
    NSData *pubKey = [defaults dataForKey:@"publicKey"];
    RSA *rsa = [RSA shareInstance];
    [rsa deleteAsymmetricKeys];
    if (privKey == nil) {
        NSLog(@"No key stored");

        NSLog(@"Generating new keys... please wait");
        self.aesPwdTF.text = @"generating keys...please wait";
        [self.textTF setEnabled:false];
        [self.aesPwdTF setEnabled:false];
        [rsa generateKeyPairRSACompleteBlock:^{
            NSLog(@"Keys prepared");
            self.aesPwdTF.text = @"";
            [self.textTF setEnabled:true];
            [self.aesPwdTF setEnabled:true];
            [self.aesPwdTF becomeFirstResponder];
            [defaults setObject:[rsa privateKeyBits] forKey:@"privateKey"];
            [defaults setObject:[rsa publicKeyBits] forKey:@"publicKey"];
    } else {
        [rsa setPrivateKey:privKey];
        [rsa setPublicKey:pubKey];
        self.publicKeyTF.text = [[rsa publicKeyBits] base64EncodedString];
        NSLog(@"Private key %@", [[rsa privateKeyBits] base64EncodedString]);
        NSLog(@"read keys from defaults");



Das ist der Teil, der entscheided, ob der Key noch mal eingelesen werden soll. Klar, man k├Ânnte das auch im Keystore drin lassen, aber leider kann ich die daten ja nicht auslesen, denn hier klemmt es jedes Mal in der einen oder anderen Form:

[codesyntax lang="objc"]

- (void)setPrivateKey:(NSData *)privateKey {
    privateKeyRef = NULL;
    OSStatus sanityCheck = noErr;
    SecKeyRef peerKeyRef = NULL;

    LOGGING_FACILITY( privateKey != nil, @"Private key parameter is nil." );

    NSMutableDictionary *peerPrivateKeyAttr = [[NSMutableDictionary alloc] init];

    [peerPrivateKeyAttr setObject:(__bridge id) kSecClassKey forKey:(__bridge id) kSecClass];
    [peerPrivateKeyAttr setObject:(__bridge id) kSecAttrKeyTypeRSA forKey:(__bridge id) kSecAttrKeyType];
    [peerPrivateKeyAttr setObject:[NSNumber numberWithBool:YES] forKey:(__bridge id)kSecAttrIsPermanent];
    [peerPrivateKeyAttr setObject:privateTag forKey:(__bridge id) kSecAttrApplicationTag];
    [peerPrivateKeyAttr setObject:privateKey forKey:(__bridge id) kSecValueData];
    [peerPrivateKeyAttr setObject:[NSNumber numberWithBool:YES] forKey:(__bridge id) kSecReturnPersistentRef];

    sanityCheck = SecItemAdd((__bridge CFDictionaryRef) peerPrivateKeyAttr, (CFTypeRef *) &peerKeyRef);
    if (sanityCheck) {
        if (sanityCheck != errSecDuplicateItem)
        // Already have a key with this digest, so look it up to get its ref:
        [peerPrivateKeyAttr removeObjectForKey: (__bridge id)kSecValueData];
        [peerPrivateKeyAttr setObject: privateTag forKey: (__bridge id)kSecAttrApplicationLabel];//??
        [peerPrivateKeyAttr removeObjectForKey: (__bridge id)kSecReturnPersistentRef];
        [peerPrivateKeyAttr setObject: (__bridge id)kCFBooleanTrue forKey: (__bridge id)kSecReturnPersistentRef];
        SecItemCopyMatching((__bridge CFDictionaryRef)peerPrivateKeyAttr, (CFTypeRef*)peerKeyRef);

    NSMutableDictionary *queryPrivateKey = [[NSMutableDictionary alloc] init];

    // Set the private key query dictionary.
    [queryPrivateKey setObject:(__bridge id)kSecClassKey forKey:(__bridge id)kSecClass];
    [queryPrivateKey setObject:privateTag forKey:(__bridge id)kSecAttrApplicationTag];
    [queryPrivateKey setObject:(__bridge id)kSecAttrKeyTypeRSA forKey:(__bridge id)kSecAttrKeyType];
    [queryPrivateKey setObject:[NSNumber numberWithBool:YES] forKey:(__bridge id)kSecReturnRef];
    ((__bridge CFDictionaryRef)queryPrivateKey, (CFTypeRef *)&peerKeyRef);
    privateKeyRef = peerKeyRef;



Das bl├Âde ist, dass nach der Ausf├╝hrung dieses Codes, sowohl im Simulator als auch auf dem Ger├Ąt die Variable privateKeyRef entweder nil ist, oder nicht f├╝r die Entschl├╝sselung verwendet werden kann.

Aber ich wollte ja eh eine "sch├Ânere" Schnittstelle bauen, die das ganze etwas entmystifiziert...

You don't own it, 'till you make it

Also, wie funktioniert eigentlich RSA. So kompliziert ist das eigentlich gar nicht, allerdings handelt es sich dabei um sehr gro├če Primzahlen. Hier eine Beispielimplementierung in Java:

[codesyntax lang="objc"]

public class RSA {
   private final static BigInteger one      = new BigInteger("1");
   private final static SecureRandom random = new SecureRandom();

   private BigInteger privateKey;
   private BigInteger publicKey;
   private BigInteger modulus;

   // generate an N-bit (roughly) public and private key
   RSA(int N) {
      BigInteger p = BigInteger.probablePrime(N/2, random);
      BigInteger q = BigInteger.probablePrime(N/2, random);
      BigInteger phi = (p.subtract(one)).multiply(q.subtract(one));

      modulus    = p.multiply(q);
      publicKey  = new BigInteger("65537");     // common value in practice = 2^16 + 1
      privateKey = publicKey.modInverse(phi);

   BigInteger encrypt(BigInteger message) {
      return message.modPow(publicKey, modulus);

   BigInteger decrypt(BigInteger encrypted) {
      return encrypted.modPow(privateKey, modulus);

   public String toString() {
      String s = "";
      s += "public  = " + publicKey  + "n";
      s += "private = " + privateKey + "n";
      s += "modulus = " + modulus;
      return s;

   public static void main(String[] args) {
      int N = Integer.parseInt(args[0]);
      RSA key = new RSA(N);

      // create random message, encrypt and decrypt
      BigInteger message = new BigInteger(N-1, random);

      //// create message by converting string to integer
      // String s = "test";
      // byte[] bytes = s.getBytes();
      // BigInteger message = new BigInteger(s);

      BigInteger encrypt = key.encrypt(message);
      BigInteger decrypt = key.decrypt(encrypt);
      System.out.println("message   = " + message);
      System.out.println("encrpyted = " + encrypt);
      System.out.println("decrypted = " + decrypt);


(Gefunden hier)

Eigentlich ist es ja nur n├Âtig, ein paar Primzahlen zu generieren, die man dann potenziert und den modulo berechnet, sowohl zum Ver- als auch Entschl├╝sseln.

Klingt eigentlich total simpel, aber die Herausforderung liegt darin, mit Zahlen zu rechnen, die mehrere 1000 bit lang sind - man muss also ne eigene Arithmetik abbilden.

Das kann beliebig komplex werden, weshalb ich mich bei meiner Implementierung an die GNU-Java BigInteger Implementierung gehalten habe und diese mehr oder minder auf Objective-C portiert habe (Source Code dafür ist hier).

Die Portierung ist nicht wirklich einfach gewesen, vor allem weil Java und Objective-C / C++ eine andere Vorstellung von primitiven Datentypen haben, aber zumindest l├Ąuft die BigInteger Arithmetik erst mal so weit.

Nachdem ich eine BigInteger Implementierung in Objective-C zur Verf├╝gung hatte, ist die Verschl├╝sselung ├Ąhnlich simpel wie in java:

[codesyntax lang="objc"]

- (BigInteger *)encryptBigInteger:(BigInteger *)message {
    if (message.bitLength > self.bitLen) {
        NSLog(@"Encrypting impossible: Message is too long for key! Key is %d bits wide, message is %d", self.bitLen, message.bitLength);
        //splitting it? how?
        return nil;
    return [message modPow:self.e modulo:self.n];

- (BigInteger *)decryptBigInteger:(BigInteger *)message {
    return [message modPow:self.d modulo:self.n];


Wobei da sicherlich noch ein paar Optimierungen passieren k├Ânnten, wie z.B. das Splitten zu gro├čer Eingaben in mehrere BigIntegers mit der richtigen Gr├Â├če f├╝r die Verschl├╝sselung.

Ich denke, das Prinzip von RSA ist bekannt und kann so Anwendung finden. Ich werde hier sicherlich noch mehr Code posten der euch dann vielleicht auch ne Menge Arbeit erspart.

Was jetzt noch fehlt sind mehr oder minder 2 Dinge: Das umwandeln von NSData in eine Menge von BigIntegers sowie der zugeh├Ârige umgekehrte Weg. Wenn das funktioniert, kann man jeden BigInteger-Wert einzeln ver- bzw. entschl├╝sseln und kann somit beliebige Daten sicher ├╝bertragen oder ablegen.

Aber dar├╝ber ein andern mal mehr...

Warum das ganze?

Naja, zum einen hat es mich stark interessiert, mal so ein verschl├╝sselungsverfahren umzusetzen - und da ich in Objective-C keine passende Implementierung gefunden habe (einen Haufen Implementierungen in c/c++ ja, aber keine wirklich objektorientiert, die man als Java-Entwickler leichter versteht), lag es doch besonders Nahe, das gleich mal in Objective-C zu implementieren.

Eine Anwendung daf├╝r schwebt mir auch schon vor, mal sehen, in wie weit dass dann wirklich seinen Weg in den App-Store findet... so "stay tuned" ;-)


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