Performance Battle of NoSQL blob storages #2: Apache Kafka

The first article of this series brought scaling factors for blob-based content on Apache Cassandra. It’s well know piece of software, requiring full installation on nodes, management application and so on. You also need to tune various configs to achieve best performance results. I’ve spend nice time playing with yamls on ubuntu 🙂

The configuration is sometimes tricky. I was little bit confused once or twice so I planned to hire Cassandra guru to our team as issues I encountered seems really complicated 🙂

Well, we do not need much functionality in HP Service Virtualization. The core is to replicate messages to achieve reliability. The next steps is to process them. Yes, the last part is to aggregate the results. Sounds exactly like map and reduce ala hadoop. Evaluation of Apache Storm or Apache Samza are different stories, but they allow me to find Apache Kafka.

Kafka is pretty nice software, much more simpler comparing to Cassandra how I’ve described it above. The only operation to use it is to depend on kafka jars in your pom files. That’s it! Maven downloads couple of dependent jar files and your environment is (almost) ready.

As you can see below, Kafka is incredibly fast, much more faster than Cassandra. Last year I read some article. An author labels Redis as most incredible software he had met so far. I agreed. Now there are two candidates for this label 🙂

It was also very beneficial to read their technical documentation for me as for technical engineer. The guys did performance research how to store data on hard drives and they also employed founded approaches in Kafka implementation. Their documentation contains interesting technical papers, like e.g. lmax pdf does with disruptor.

Setup

The setup was identical to the described in first article.

Performance Measurement of Kafka Cluster

Batch Size

  • 8 topics
  • two partitions per one topic
Blob size \ Batch Size [TPS] 128 256 512 1024 32768
100 b 392k 432k 433k 495k 396k
20 kb 9k

There is little difference between various batch sizes so you can tune the value according your needs. Note that overall throughput is incredible: raw 400 mbits/s.

Number of Connections

  • batch size is 512

Blob size \ Connections [TPS per connection] 1 2 4 8 16 32
100 b 84k 74k 62k 58k 37k 17k
20 kb 3.2k 2.5k 1.7k

You can see that number of connections significantly increases the throughput of Kafka during writes. Two connections handles 150k but, 8 ones allows 464k messages per all connections.

Number of Partitions

  • 8 connections

Blob size \ Partitions [TPS] 1 2 4 8 16 64
100 b 535k 457k 493k 284k

Two partitions brought little difference in the overall score. There is same approach or pattern like in hyper threading.

Long Running

The goal of this test is to verify the behavior once the cluster is under the continual heavy load for couple of minutes – as the underlying storage goes out of space (150GB). As Kafka is really fast, such space lasts couple of minutes.

Blob size \ [TPS]
100b 490k

The test generates 150GB of data successfully stored by Kafka. The throughput result is almost the same as in short test.

Replication

This is crucial verification how is Kafka affected by the replication.

Blob size \ Replicas [TPS] 1 2
100 b 577k 536k

There is difference less than 10% when two nodes are involved in the replication. Great! Both nodes handles 400mbits throughput.

Large Messages

Previous tests use relatively small messages, how does it behave with larger message?

Blob size \ [TPS]
100b 640k
20 kb 6k
500kb 314

The best throughput achieved last – large – message. Even for so large entity, it handles unbelievable 1.2Gbits/s. Note that all of this is a remote communication, we have 10gbps network.

Occupied Space

As the kafka stores byte array, the occupied space for this system depends on the serialization framework. I used famous kryo.

Blob size \ [Occupied space in bytes per message]
100b 183b

Here is the structure of serialized entity.
class Message {

    private UUID messageId;
private UUID virtualServiceId;
private String targetUrlSuffix;
private int responseStatusCode;
private long time;
private byte[] data;
}

Conclusion

Kafka surprised me a lot. It’s performance is incredible. The installation is just the dependency on a jar file, the configuration is very easy. The API is really simple, It’s up to you what kind and form of the content you prefer.

The only drawback is that this piece of software is pretty young. In the time this article was being written, beta version 0.8 is out. For example, async API is now in a proposal only.

On the other hand, there is large set of various articles, videos and other materials, how one used it in his project, especially along with Apache Storm.

Well,  if you want to use messaging within your new solution you should definitely look at Apache Kafka.

Performance Battle of NoSQL blob storages #1: Cassandra

Preface

We spend last five years on HP Service Virtualization using MsSQL database. Non-clustered server. Our app utilizes this system for all kinds of persistence. No polyglot so far. As we tuned the performance of the response time – we started at 700ms/call and we achieved couple milliseconds per call at the end when DB involved – we had to learn a lot of stuff.

Transactions, lock escalation, isolation levels, clustered and non clustered indexes, buffered reading, index structure and it’s persistence, GUID ids in clustered indexes, bulk importing, omit slow joins, sparse indexes, and so on. We also rewrite part of NHibernate to support multiple tables for one entity type which allows use scaling up without lock escalation. It was good time. The end also showed us that famous Oracle has half of our favorite features once we decided to support this database.

Well, as I’m thinking about all issues which we encountered during the development, unpredictive behavior is the feeling I have once I did all this with my DB specialist colleague. When we started to care about the performance more and more we must have understood those features and we usually took out default behavior and plug-in some special one – we could have done that as we understood essentials of the system. This is the way.

As I’ve described those features above, a lot of NoSQL storages allow to use them on your own. You can easily provide inverted index in key-value store even if the database itself does not support such functionality. What you need to do is to write error-less code and maintain indexes by your own code. Look at twitter example for famous key-value store Redis. It is not so simple as three/four tables in MySql with build-in indexing, isn’t it?

There is nothing bad about SQL databases. I still believe that they fit the purpose of 80% of programs as they are best for difficult querying and/or less than massive scaling. But who need real massive scaling? Everyone?

Why we are going to use NoSQL?

Here in this article and the whole series, I’m going to evaluate certain attributes of storages for persistence of binary data of variable length. The goal is to persist network traffic in binary data format.
No relation decomposition, no querying of particular attributes of particular messages. No implicit transactions complying with ACID. No indexes and so on. You can find the reasons in the preface, we just hope that we know what we do 🙂

Cassandra

The first evaluated storage is Cassandra. It’s famous NoSQL database, commercial support provides virtual confidence for fast support and so on.
Cassandra is somewhere in the middle between schemaless and schemaware (SQL like) databases as it supports schema and CQL query language. I was pretty surprised when I have saw features like time series with dynamic columns, build-in lists and sets. Next good stuff is OpsCenter monitoring tool, I always show some screenshots from the application when I want to demonstrate easy-of-use monitor for some distributed app. Pretty nice.

Hardware

Hardware used within the tests:
  • DB cluster:
    • HP Proliant BL460c gen 8, 32core 2.6 GHZ, 32GB RAM, W12k server
    • HP Proliant BL460c gen 8, 32core 2.6 GHZ, 192GB RAM, ubuntu 12 server
  • Tests executor:
    • xeon, 16 cores, 32gb ram, w8k server
  • 10Gbps network
  • Java 7

Performance Measurement of Cassandra Cluster

The setup used one seed and one replica, java tests use datastax driver. Replication factor two when not explicitly defined. After some issues:

//platform.twitter.com/widgets.js

… my final CQL create script was:


  1. CREATE COLUMNFAMILY recording (



  2. PARTKEY INT,



  3. ID uuid,



  4. VS_ID uuid,



  5. URL ascii,



  6. MESSAGE_TIME BIGINT,



  7. DATA BLOB,



  8. PRIMARY KEY((PARTKEY, VS_ID), ID)



  9. );



Batch Size

Datastax driver allows to use queries in batches, how does it scale?

Blob size \ Batch Size [TPS] 128 256 512 1024 8196 32768
100 bytes (8 clients) 67 100 63 900 55 400 55 800 23 500 Timeouted
1 kb (4 clients) 15 600 20 600 20 300 19 400 5 500 N/A
5 kb (1 client) N/A 8 300 9 200 10 000 3 000

256 rows seems like best size of the batch.

Number of Connections

The tests utilizes variable count of connections to the Cassandra cluster. Note that the batch size was 256 and consistency was set to ONE.

Blob size \ Connections [TPS] 1 2 4 8 24
100 b 27 000 53 000 75 000 62 000 62 000
1 kb 17 900 31 000 42 000 40 000
5 kb 9 000 14 600 16 000 10 300
20 kb 3 300 4 500 3 800

You can see that 2 or 4 connections per one client are enough whilst one connection slows down the overall performance.

Number of Partitions

The table contains partition key, see the CQL code snippet. The test generates variable values for the partition key. Batch size was also 256 and consistency one.

Blob size \ Partitions [TPS] 1 8 64
100 b 58 000 71 000 70 000
10 kb 13 200 9100 7 300
Partition key says where the data will reside, it defines target machine. As two nodes are involved in my tests, its better to use more than one partition.

Variable Consistency

Tuning of consistency is the most beautiful attribute in Cassandra. You can define consistency level in every aspect of the communication with cassandra driver in any language. There are different levels, see the documentation.

The test used 8 partitions, 8 connections and 256 batch size.

Blob size \ Consisency [TPS] Any One Two
100 b 67 000 66 000 66 000

Well, I’m surprised. There is almost no difference between consistency levels for such blob size despite totally different purpose or implementation. This could happen due to almost ideal environment, very fast network and machines as well.

Variable Replicas

I used two replicas by default but this tests tries to reveal the price for second copy of the data.

Blob size \ Replicas [TPS] 1 2
100 b 121 000 72 000
10 kb 19 200 8 200

There is apparent penalty for second replica.

Long Running

Previous tests used a few millions of inserts/rows but the current one uses hundred of millions. This is the way how to definitely omit the advantage of various caches and clever algorithms. This simulates heavy weight load test to the server.

8 partitions, 8 connections, 256 batch size.

Blob size \ Replicas+Consistency [TPS] 1+ONE 2+ONE 2+TWO
100 b (1 billion) 121 000 63 000 62 00
10 kb (30 m) 8 200

Large Messages

The largest message in these tests has 20 kb so far, but what is the behavior when our app would meet some large file as a attachment?
Consistency One, 2 partitions, 2 connections, 2 batch size.

Blob size  [TPS]
500 kb 90
5 mb 9.5

Occupied Space

Apache Cassandra automatically enables compression since 1.1 version.

Blob size \ [Occupied space in bytes per message]
100b 89b

Here is the structure of serialized entity.

CREATE COLUMNFAMILY recording (
    PARTKEY INT,
    ID uuid,
    VS_ID uuid,
    URL ascii,
    MESSAGE_TIME bigint,
    DATA blob,
    PRIMARY KEY((PARTKEY, VS_ID), ID)
);

Conclusion

This kind of testing is almost opposite in comparison to neflix tests. They analyzed scalability but we have done testing of simple cluster having two machines only. We need to figure out the performance of one node (obviously in cluster deployment).
I was impressed as the numbers above are good, the overall throughput on the network reached hundred mbps coming out from one client. 
What next. Same kinds of tests agains Kafka, Redis, Couchbase, Aerospike, Couchbase.

Kryonet: simple but super-fast TCP communication in Java + performance benchmarks

We have designed cluster-aware and cloud-based solution of service virtualization for some time. Incredible work as distributed system is next dimension of thinking – and learning as well 🙂

We was deciding to split atomic piece of our domain work to multiple nodes. Such step would hopefully provide us to point certain messages to certain nodes. We can than store data in main memory and avoid distributed locking above some remote store, probably Redis. This is usual approach how to achieve better response time of the whole solution to avoid database search for all calls. Obviously with well designed replication.

I was really worried about latency. We need really low response time in couple of milliseconds at most under heavy load. I’m talking about the whole solution which consumes CPU a lot because of the calculations of simulated response under the hood, so no space for any wasting.

What is the best latency of such transmission between two nodes? What is the throughput of one or multiple connections? Except theoretical analysis I also did our own proof of concept to verify our thoughts in the reality.

I like kryo serialization framework. It’s very fast, very simple and the boot of anybody is in level of minutes. Hopefully I discovered that guys from Esoteric Software think about remote communication and they did very logic step. They developed framework allowing TCP communication using kryo serialization – kryonet.

Well, I believe that nobody is surprised that the solution is very fast and super easy as well. Like kryo itself.

There are some benchmarks how TCP communication can be fast, in level o microseconds, but what is the real response time, performance, when I need to transfer some domain object to another node in real environment using gigabit network. I mean no laboratory environment but just real one where our app will probably run in couple of months?

Well, in short it’s hundred of microseconds.

Kryonet via Kryo

It is built above java nio, kryo serialization and asynchronous approach when the response is being received.
It’s better to read the manual on github as coding around the sending and the receiving is very easy.

Kryo preserves the way that the serializators are separated classes, later registered to the system, so you do no have to affect your domain classes. But obviously, you have to open them to support injections of fields from outside. Or you can use the reflection too.

The response is received asynchronously to the processing of the original request. Kryonet exposes TCP port on the server side and uses TCP under the hood. But don’t worry, you almost can’t touch complexity of TCP messaging.

Number meaning and setup

What was my message? It contains identifier, uuid, and map of elements. The element contains the data but the important thing is the overall size of the message.

Response time is the whole round-trip:

  1. Serialization on the client side
  2. To send the request to the server
  3. Deserialization of the message on server side
  4. To respond the message identifier back to the client
  5. The deserialization of received message on the client size

Well, the response time is just the number you can expect when you want to push some message to remote component and wait for the acknowledgment – ACK.

Both client and server are HP Proliant, 24 and 32 cores, >1 gigabit network.

Performance Benchmarks

One TCP Connection

First of all, I used one TCP connection.

Message Size [bytes]/Number of elements Average RT [micro-seconds] Median RT [micro-seconds] Buffer size Throughput [msgs/s] Rough Load [msgs/s]
48 / 2 190 160 256 B 5400 100k
48 / 2 202 170 16 kB 5400 100k
48 / 2 672 157 1kB 18300 1M
48 / 2 418 130 16kB 19300 4M
3216 / 200 316 313 16 kB 650 1k
3216 / 200 332 331 256 B 1k

Message size: size of serialized message into raw byte[].

Average vs. median: as my test keeps all response time value, it was very interesting to analyze them. Average value reflects all edge cases, mostly the divergence during the flushing of buffers etc., but median value does not. What does it mean in real? The difference between average and median value indicates growing number of slower responses.

Buffer: kryo uses serialization and object buffer, see the documentation.

Rough load: all calls are asynchronous which means that I had to limit incoming load generated by my generator. The approach I chose was to call standard java thread sleep after certain messages. The operation in milliseconds is not quite accurate but it was fine for rough limitation of input load. 4M means that the generator thread sleeps for 1 milliseconds once it generates four thousand of messages.

The discussion:

  • there is only two times slower response time for 100 times larger message
  • there is almost no different response time for various buffer sizes
  • increasing load means almost the same median value but highly increased average response time. Well, the response time is not stable at all which indicates that you can’t rely on response time in the case of massive load

Multiple Client Connections to One TCP Server Port

The approach having multiple clients using one TCP port usually scales well, but not for kryonet as it uses limited buffer size, see following table. I used 10 concurrent clients for the measure.

Message Size [bytes]/Number of elements Average RT [micro-seconds] Median RT [micro-seconds] Buffer size (client/server) Throughput [msgs/s] Rough Load [msgs/s]
48 / 2 256 254 16k/16k 9000 1k
48 / 2 580 535 16k/160k 42000 5k
48 / 2 33000 23000 16k/300k 80000 10k

The discussion:

  • kryo itself is very fast but single threaded deserialization brings certains limits so huge throughput was achieved for the cost of really high latency
  • the increasing of the buffer size within the server allows to serve much more requests as they just fall into buffer and were not rejected as before for lower buffer size

Multiple Connections to Multiple Ports

The last includes multiple clients connected to the multiple server ports. It brought the best performance as expected.

Message Size [bytes]/Number of elements Connections Average RT [micro-seconds] Median RT [micro-seconds] Buffer size (client/server) Throughput [msgs/s] Rough Load [msgs/s]
48 / 2 10 542 486 256/256 54000 100k
48 / 2 10 5000 518 16k/16k 155000 500k
48 / 2 20 2008 1423 16k/32k 210000 500k
48 / 2 50 1460 404 16k/32k 182000 100k
48 / 2 50 2024 378 256k/256k 220000 100m
48 / 2 100 3120 404 256k/256k 231000 100m
3216 / 200 10 405 368 8k/32k 21000 3k
3216 / 200 10 415 353 16k/32k 21000 3k
3216 / 200 20 399 350 16k/32k 32300 2k
3216 / 200 50 380 327 16k/32k 43700 1k
3216 / 200 50 410 344 64k/128k 78000 2k

The discussion:

  • incredible load for 100/50 clients per second in no bad time as I would originally expected
  • as I’ve already discuss the stuff above, there is huge different between median and average RT as well
  • there is no such difference between 20 and 100 clients in throughtput but in average/median RT

Resources

Well, usually the transport of a message from one to another node is not your business case. It’s important that both client and server is able to do it’s core logic – message processing of received throughput is always the core.
One client does not almost burden the server, see htop screenshot for the most approach having the best throughput:
On the other hand, 10 concurrent clients to one server side port is almost the same even if the number of transported messages is 5x time higher:
As you can see, 50 concurrent clients and ports is more likely theoretical stuff for this measure than usable solution as the server do not any other thing then deserialize incoming throughtput 🙂

Conclusion

It’s fast and easy. Almost 20k transmitted small messages for one client per second is incredible number for me. On the other hand, 150-200 micro seconds response time is surprise for me as well.
The only thing to consider is to carefully define the size of all buffers. There is no queuing and just the rejection of a message can happen if the connection is under heavy load.

Coursera: Functional Programming Principles in Scala by Martin Odersky

It’s almost half year when I found a link on Twitter when someone pointed out to new course of functional programming in Scala. The course prepared Martin Odersky who also recorded all video materials. All enthusiastic programmers know that Odersky is author of Scala programming language. I’ve studied hard so after couple weeks I obtained a certificate. I have to note that it was my first meet with coursera itself and it left as best impressions as it could. But lets start from the beginning.

The course took 7 weeks. There was also first, zero, week which supposed to define targets, make people to understand topics etc. Everyone could also follow steps how to setup scala environment from the scratch along with Eclipse and SBT. SBT build system was new for me and I already put it into my notes as thing I need to study later. Contrary to course recommendations I used IntelliJ Idea all the time and I’ve not met any problem. It was just about the first setup of your environment but nothing new for me as I already made few REST services in scala using Spray. But back to the course itself.

Martin Odersky published couple of videos every week. The total length was about 1 hour (per a week) so nothing which would consume some significant part of your time. I’ve already have couple real experiences, some books too, with Scala so first weeks was crucial for me. As the name of course indicates, it’s not only about scala itself but about functional programming. This was the point of first weeks. I finally found out basic terms, designs and flows how modern functional programming works. Well, everyone attended some course regarding functional programming at a college but this was really good.

The work of every week was covered by one or more exercises. Despite my original feeling I have to say that those exercise were brilliant and seems for me that it was the crucial part of overall understanding. My original feeling was much more worse that today – three months after the end of course. Why? It seemed for me as little bit academical. What’s going on?

Odersky and his team prepared skeleton in scala, provide couple tests in the solution and lead you to provide the rest of missing programming code. The solution was compilable all the time but those tests where failing. You supposed to read the tutorial for an assignment and put missing code in there. The difficulty was changing in the time, especially assignment at the end of the whole course was little bit hard but everyone will understand working with collections or stream approach in scala to death 🙂

The problem I pointed out few lines above is that I spent a lot of time with reading and undestanding of the problem itself, e.g. how works very popular game of infection, or how they design the skeleton, e.g. words decomposition in the assignment of anagrams.

Once you was satisfied with your solution you just use git with SBT’s commit and send your solution to repository. I’ve already written that original solution already contained few tests. The lecturer developed more non-public test used to testing when someone committed his solution. This sometimes arouse passion because you have developed your solution, your tests works so you decided to commit it to coursera’s repository. After couple minutes when their tests were done you found out that your solution is not working for large data. What to do now? You do not have that test, you even do not have attributes of large test. One can just look into his code and try to find problem without any real point where to start.

The last thing was the only issue on perfect course in all other respects. There was also a follow-up in the form of course about Reactive Programming. Thing went around Akka, async/await, Futures and promises and so on.

I really enjoyed the course. After few moths I have to say that it’s much more efficient than any kind of reading – obviously because of assignments. It turns out into my almost primary target where to learn new things as there is multiple things to learn.

Redis messaging using Spring and Kryo serialization

Redis is famous enterprise key/value data store which provides simple messaging using publisher/subscriber. I’ve decided to use the system to notify remote nodes (components) about a change of state between those components.

Spring among the others allows to use it’s templating system to decouple business logic from messaging system used under the hood. The use of spring data for Redis guarantees a solution which does not utilize any redis command in the code.

Redis contains serialized content, either byte[] or string. So the last thing to reveal is domain model serialization. I’ve decided to fast binary serialization using Kryo framework as a winner of battle of serializators.

Maven

First of all, it’s necessary to define all dependant components. Obviously, usual component like spring itself missing.

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com.esotericsoftware.kryo
kryo
2.22


org.springframework.data
spring-data-redis
1.1.0.RELEASE


redis.clients
jedis
2.1.0

Kryo Serializator

I used domain model’s entity which hold identifier of our internal service, this is just UUID. The point is to setup kryo serializator which get the entity and returns byte[]. The entity itself is not important.

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public class MyEntitySerializer extends Serializer<MyEntity> {

@Override
public void write(Kryo kryo, Output output, MyEntity object) {
return ...;
}

@Override
public MyEntity read(Kryo kryo, Input input, Class<MyEntity> type) {
return ...;
}
}

Redis Message Handler

The beautiful part is the complete definition of async message handling within xml configuration of redis message container.

Note that I used Jedis client as java redis client.

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<bean id="jedisConnectionFactory" class="org.springframework.data.redis.connection.jedis.JedisConnectionFactory"
p:host-name="${redisHost}" p:use-pool="true"/>

<bean id="myEntitySerializer" class="MyEntitySerializer" />

<bean id="redisContainer" class="org.springframework.data.redis.listener.RedisMessageListenerContainer">
<property name="connectionFactory" ref="jedisConnectionFactory"/>
<property name="messageListeners">




<bean id="messageListener"
class="org.springframework.data.redis.listener.adapter.MessageListenerAdapter">

<bean class="MyEntityListener"
autowire="constructor"/>

<property name="serializer" ref="myEntitySerializer"/>


<bean class="org.springframework.data.redis.listener.ChannelTopic">
<constructor-arg value="${channelName}"/>





What is going on?

Message container references both redis factory and given list of message listeners. It’s a class (or set of classes) having registered method as a callback when a new message arrives.

The last property is channel with important channel name. The code uses two variables, first is redis host and already mentioned channel name.

Message Listener

The last thing to do is to define message listener containing callback method, MyEntityListener. The class instance is called always once new message arrive using channel topic.

Crucial point was to discover the signature of callback’s method because spring’s documentation is little bit sloopy. Quick look into org.springframework.data.redis.listener.adapter.MessageListenerAdapter’s onMessage shows the correct way.

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public class MyEntityListener {

public void handleMessage(MyEntity entity, String channel) {
// provide handling code
}
}

Incoming message is automatically deserialized so the method accepts entity itself.

Conclusion

Look at previous code. Every redis related code is defined in spring’s context and container class. No boilerplate code. Pretty nice.

Architectural note should explicitly show that the entity serves as a notification only. This is very important as such messaging is not reliable. Although the entity holds some information all the persistent data is defined within another place, e.g. as key/value pairs. Approaching message just notifies subscriber that new content is available to refresh and it’s supposed to GET key(s).

Scala: get rid of not-null validations

Scala has been always known as a language which allows special handling of null values. There is ton of articles regarding Some[T], None, Option[T].

What is most annoying code for me? Null validations, see usual example:

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class Entity {

}

class ServiceA {
def method(a: Entity, b: Entity, c: Entity, d: Entity) = {
Validate.notNull(a)
Validate.notNull(b)
Validate.notNull(c)
Validate.notNull(d)
}
}

class ServiceB(val a: ServiceA) {
def method() = {
a.method(null,null,null,null)
}
}

When you start to write save code in the term of fails-as-fast-as-possible, your code, services or even domain model, will be weedy, you will find such boilerplate code at every method because you can’t be sure which parameter supplied someone to you class or method.

Unfortunately Scala has beautiful way how to achieve nice and simple code – without these checks. If you don’t think that null is proper state for your class, just dismiss this option. How to do that?

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class Entity extends NotNull{

}

Well, that’s all. Look at screenshot from my IDE what is about to happen:

notNull trait scala

Now, if you are sure that you don’t want to allow null value for your entity, you can implement NotNull trait and you can remove many lines from your source code.

I was surprised when I found this trait in scala code, because many tutorials or even famous Scala for the Impatient book does not mention this simple but beautiful piece of code.

Tomcat 7 remote deployment

I decided to provide automatic deployment of war packaged application using Jenkins and Deployment plugin. The target platform is Amazon with Tomcat 7, see nice set of articles to find out how to setup such environment for free.

Well, there is couple of tutorials but they missing some points so it pushed me to lost one hour of my work.

What I got

  • Fresh installation of Tomcat 7 on remote machine with opened 8080 port on firewall
  • Personal war file supposed to be deployed

How to push it to tomcat?

1. First of all, there is simple configuration of tomcat users in file tomcat-users.xml – it was my pain in the ass 🙂 As original comprehensive documentation says, it’s necessary to define user, but which one(s)?

Here is working example of tomcat-users.xml:




The important part is manager-script, contrary to Tomcat 6 where it had not exist yet. This user allows access to /text sub-namespace in management uri. The first user called manager-gui is the one which you use in GUI console, e.g. http://localhost:8085/manager/html

Once you run tomcat using bin’s bat file, you can move to second bullet.

2. Now, it’s possible to use remote deployment using curl command, e.g. in my use-case:

curl –upload-file my.war “http://manager-script:changeit@localhost:8080/manager/text/deploy?path=/myPath&update=true&#8221;

The command is working using manager-script user contrary to my original manager-gui. Another interesting part is path=/myPath. This attribute say which URL sub-namespace is to be used.

Even if you deploy my.war and common Tomcat’s approach is to deploy application in /my subname, the application is to be exposed on /myPath.