[OracleCode SF] In memory analytics with apache spark and hazelcast

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Apache Spark is a distributed computation framework optimized to work in-memory, and heavily influenced by concepts from functional programming languages. Hazelcast - open source in-memory data grid capable of amazing feats of scale - provides wide range of distributed computing primitives computation, including ExecutorService, M/R and Aggregations frameworks. The nature of data exploration and analysis requires data scientists be able to ask questions that weren't planned to be asked—and get an answer fast! In this talk, Viktor will explore Spark and see how it works together with Hazelcast to provide a robust in-memory open-source big data analytics solution!

Technology

@gamussa @hazelcast #oraclecode
IN-MEMORY ANALYTICS
with APACHE SPARK and
HAZELCAST

@gamussa @hazelcast #oraclecode
Solutions Architect
Developer Advocate
@gamussa in internetz
Please, follow me on Twitter
I’m very interesting ©
Who am I?

@gamussa @hazelcast #oraclecode
What’s Apache Spark?
Lightning-Fast Cluster Computing

@gamussa @hazelcast #oraclecode
Run programs up to 100x
faster than Hadoop
MapReduce in memory,
or 10x faster on disk.

@gamussa @hazelcast #oraclecode
When to use Spark?
Data Science Tasks
when questions are unknown
Data Processing Tasks
when you have to much data
You’re tired of Hadoop

@gamussa @hazelcast #oraclecode
Spark Architecture

@gamussa @hazelcast #oraclecode
Resilient Distributed Datasets (RDD)
are the primary abstraction in Spark –
a fault-tolerant collection of elements that can be
operated on in parallel

@gamussa @hazelcast #oraclecode
RDD Operations

@gamussa @hazelcast #oraclecode
operations on RDDs:
transformations and actions

@gamussa @hazelcast #oraclecode
transformations are lazy
(not computed immediately)
the transformed RDD gets recomputed
when an action is run on it (default)

@gamussa @hazelcast #oraclecode
RDD
Transformations

@gamussa @hazelcast #oraclecode
RDD
Actions

@gamussa @hazelcast #oraclecode
RDD
Fault Tolerance

@gamussa @hazelcast #oraclecode
RDD
Construction

@gamussa @hazelcast #oraclecode
parallelized collections
take an existing Scala collection
and run functions on it in parallel

@gamussa @hazelcast #oraclecode
Hadoop datasets
run functions on each record of a file in Hadoop distributed
file system or any other storage system supported by
Hadoop

@gamussa @hazelcast #oraclecode
What’s Hazelcast IMDG?
The Fastest In-memory Data Grid

@gamussa @hazelcast #oraclecode
Hazelcast IMDG
is an operational,
in-memory,
distributed computing platform
that manages data using
in-memory storage, and
performs parallel execution for
breakthrough application speed
and scale

@gamussa @hazelcast #oraclecode
High-Density
Caching
In-Memory
Data Grid
Web Session
Clustering
Microservices
Infrastructure

@gamussa @hazelcast #oraclecode
What’s Hazelcast IMDG?
In-memory Data Grid
Apache v2 Licensed
Distributed
Caches (IMap, JCache)
Java Collections (IList, ISet, IQueue)
Messaging (Topic, RingBuffer)
Computation (ExecutorService, M-R)

@gamussa @hazelcast #oraclecode
Green
Primary
Green
Backup
Green
Shard

@gamussa @hazelcast #oraclecode
final SparkConf sparkConf = new SparkConf()
.set("hazelcast.server.addresses", "localhost")
.set("hazelcast.server.groupName", "dev")
.set("hazelcast.server.groupPass", "dev-pass")
.set("hazelcast.spark.readBatchSize", "5000")
.set("hazelcast.spark.writeBatchSize", "5000")
.set("hazelcast.spark.valueBatchingEnabled", "true");
final JavaSparkContext jsc = new JavaSparkContext("spark://localhost:7077",
"app", sparkConf);
final HazelcastSparkContext hsc = new HazelcastSparkContext(jsc);
final HazelcastJavaRDD<Object, Object> mapRdd = hsc.fromHazelcastMap("movie");
final HazelcastJavaRDD<Object, Object> cacheRdd = hsc.fromHazelcastCache("my-
cache");

@gamussa @hazelcast #oraclecode
LIMITATIONS

@gamussa @hazelcast #oraclecode
DATA SHOULD NOT BE
UPDATED WHILE READING
FROM SPARK

@gamussa @hazelcast #oraclecode
MAP EXPANSION
SHUFFLES THE DATA
INSIDE THE BUCKET

@gamussa @hazelcast #oraclecode
CURSOR DOESN’T POINT TO
CORRECT ENTRY ANYMORE,
DUPLICATE OR MISSING
ENTRIES COULD OCCUR

@gamussa @hazelcast #oraclecode
github.com/hazelcast/hazelcast-spark

@gamussa @hazelcast #oraclecode
THANKS!
Any questions?
You can find me at
@gamussa
viktor@hazelcast.com

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You never know how your application performs once deployed to the production. Sure, you have unit tests, and your test coverage is sky high. However, you might depend on external resources like databases, web services, and distributed caches. Moreover, without a proper integration testing, you cannot be confident about the stability of your production environment. A long time ago, long before the invention of Docker, configuring the environment for integration testing was incredibly complicated and tedious. Developers and Ops were using fake database implementations, mocking servers, and all kinds of weird tricks to implement the environment without the real environment, but it was ugly, bulky, faulty and not cross-platform as well. Thanks to Docker, those days are long gone, now we can quickly prepare the environment with Docker for our tests. In this talk, I would like to spread the word about awesome project TestContainers – an open source library that exposes API for JUnit tests. It provides lightweight, disposable instances of shared databases, distributed caches or grids, or anything else that can run in a Docker container, all securely and reliably downloaded from your Docker Hub.

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Viktor Gamov

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The more we work with Java 8, the more we go into the rabbit hole. Did they add all those streams, lambdas, monads, Optionals and CompletableFutures only to confuse us? It surely looks so! And Java 9 that heads our way brings even more of what we like the most, more puzzlers, of course! In this season we as usual have a great batch of the best Java WTF, great jokes to present them and great prizes for the winners!

[Philly ETE] Java Puzzlers NG

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Виктор возвращается с продолжением доклада про обработку данных в памяти! На этот раз он противопоставит «потоковый» подход «пакетному». Как мы знаем, информации, зараза, много! Мало того, что её много, так ещё никто не хочет сидеть и ждать, пока Hadoop-кластер посчитает всё. Big Data уже не интересно, Fast Data — наше новое «всё»! Так что же делать, если новые данные всё прибывают и прибывают по каналам связи (через сокет-сервер ли или модную нынче Кафку) ежесекундно (а может, даже и быстрее)? Можно было бы складывать их в какое-нибудь хранилище и считать по ночам MapReduce-ом. Или всё-таки прибегнем к потоковой обработке? У каждого подхода есть свои преимущества и недостатки: как очевидные, так и те, что не всегда лежат на поверхности. После небольшого введения и обзора терминов Виктор на основе примеров кода покажет, как использовать Hazelcast Jet для распределенной обработки потоковых данных.

Распределяй и властвуй — 2: Потоки данных наносят ответный удар

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Вы из тех, кто гонится за производительностью, запихивая в любой цикл Collection.parallelStream()? Задумываетесь, как обойтись без глобальной переделки кода и решить все проблемы с помощью большего количества серверов? Как получить распределённую обработку с линейным ростом производительности? Если подобные вопросы звучат у вас в голове, тогда этот доклад для вас. Оказывается, лишь немногие могут запускать Streams в распределенном режиме — Oracle Coherence, Infinispan, Hazelcast Jet — кто-то за деньги, кто-то бесплатно. На примере Hazelcast Jet Виктор расскажет обо всех деталях распределённой обработки. Кроме того, познакомит слушателей со своим другом Ориентированным Ациклическим Графом и покажет, как с его помощью организована обработка информации поверх API Java 8 Stream. Распределенная! Высокопроизводительная! «В памяти»!

[JBreak] Блеск И Нищета Распределенных Стримов - 04-04-2017

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There is a perception in the Java community that JavaScript is a second-league interpreted language with the main purpose of making Web pages a little prettier. But JavaScript is a powerful, flexible, dynamically typed language. And today language has been experiencing its a revival driven by the interest in HTML5. Nashorn is a modern JavaScript engine available on JVM, and it’s already included with JDK8 builds. This presentation is about building polyglot application with Java and JavaScript.

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[OracleCode SF] In memory analytics with apache spark and hazelcast

1. @gamussa @hazelcast #oraclecode IN-MEMORY ANALYTICS with APACHE SPARK and HAZELCAST

2. @gamussa @hazelcast #oraclecode Solutions Architect Developer Advocate @gamussa in internetz Please, follow me on Twitter I’m very interesting © Who am I?

3. @gamussa @hazelcast #oraclecode What’s Apache Spark? Lightning-Fast Cluster Computing

4. @gamussa @hazelcast #oraclecode Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.

5. @gamussa @hazelcast #oraclecode When to use Spark? Data Science Tasks when questions are unknown Data Processing Tasks when you have to much data You’re tired of Hadoop

6. @gamussa @hazelcast #oraclecode Spark Architecture

7. @gamussa @hazelcast #oraclecode

8. @gamussa @hazelcast #oraclecode RDD

9. @gamussa @hazelcast #oraclecode Resilient Distributed Datasets (RDD) are the primary abstraction in Spark – a fault-tolerant collection of elements that can be operated on in parallel

10. @gamussa @hazelcast #oraclecode

11. @gamussa @hazelcast #oraclecode RDD Operations

12. @gamussa @hazelcast #oraclecode operations on RDDs: transformations and actions

13. @gamussa @hazelcast #oraclecode transformations are lazy (not computed immediately) the transformed RDD gets recomputed when an action is run on it (default)

14. @gamussa @hazelcast #oraclecode RDD Transformations

15. @gamussa @hazelcast #oraclecode

16. @gamussa @hazelcast #oraclecode

17. @gamussa @hazelcast #oraclecode RDD Actions

18. @gamussa @hazelcast #oraclecode

19. @gamussa @hazelcast #oraclecode

20. @gamussa @hazelcast #oraclecode RDD Fault Tolerance

21. @gamussa @hazelcast #oraclecode

22. @gamussa @hazelcast #oraclecode RDD Construction

23. @gamussa @hazelcast #oraclecode parallelized collections take an existing Scala collection and run functions on it in parallel

24. @gamussa @hazelcast #oraclecode Hadoop datasets run functions on each record of a file in Hadoop distributed file system or any other storage system supported by Hadoop

25. @gamussa @hazelcast #oraclecode What’s Hazelcast IMDG? The Fastest In-memory Data Grid

26. @gamussa @hazelcast #oraclecode Hazelcast IMDG is an operational, in-memory, distributed computing platform that manages data using in-memory storage, and performs parallel execution for breakthrough application speed and scale

27. @gamussa @hazelcast #oraclecode High-Density Caching In-Memory Data Grid Web Session Clustering Microservices Infrastructure

28. @gamussa @hazelcast #oraclecode What’s Hazelcast IMDG? In-memory Data Grid Apache v2 Licensed Distributed Caches (IMap, JCache) Java Collections (IList, ISet, IQueue) Messaging (Topic, RingBuffer) Computation (ExecutorService, M-R)

29. @gamussa @hazelcast #oraclecode Green Primary Green Backup Green Shard

30. @gamussa @hazelcast #oraclecode

31. @gamussa @hazelcast #oraclecode final SparkConf sparkConf = new SparkConf() .set("hazelcast.server.addresses", "localhost") .set("hazelcast.server.groupName", "dev") .set("hazelcast.server.groupPass", "dev-pass") .set("hazelcast.spark.readBatchSize", "5000") .set("hazelcast.spark.writeBatchSize", "5000") .set("hazelcast.spark.valueBatchingEnabled", "true"); final JavaSparkContext jsc = new JavaSparkContext("spark://localhost:7077", "app", sparkConf); final HazelcastSparkContext hsc = new HazelcastSparkContext(jsc); final HazelcastJavaRDD<Object, Object> mapRdd = hsc.fromHazelcastMap("movie"); final HazelcastJavaRDD<Object, Object> cacheRdd = hsc.fromHazelcastCache("my- cache");

32. @gamussa @hazelcast #oraclecode final SparkConf sparkConf = new SparkConf() .set("hazelcast.server.addresses", "localhost") .set("hazelcast.server.groupName", "dev") .set("hazelcast.server.groupPass", "dev-pass") .set("hazelcast.spark.readBatchSize", "5000") .set("hazelcast.spark.writeBatchSize", "5000") .set("hazelcast.spark.valueBatchingEnabled", "true"); final JavaSparkContext jsc = new JavaSparkContext("spark://localhost:7077", "app", sparkConf); final HazelcastSparkContext hsc = new HazelcastSparkContext(jsc); final HazelcastJavaRDD<Object, Object> mapRdd = hsc.fromHazelcastMap("movie"); final HazelcastJavaRDD<Object, Object> cacheRdd = hsc.fromHazelcastCache("my- cache");

33. @gamussa @hazelcast #oraclecode final SparkConf sparkConf = new SparkConf() .set("hazelcast.server.addresses", "localhost") .set("hazelcast.server.groupName", "dev") .set("hazelcast.server.groupPass", "dev-pass") .set("hazelcast.spark.readBatchSize", "5000") .set("hazelcast.spark.writeBatchSize", "5000") .set("hazelcast.spark.valueBatchingEnabled", "true"); final JavaSparkContext jsc = new JavaSparkContext("spark://localhost:7077", "app", sparkConf); final HazelcastSparkContext hsc = new HazelcastSparkContext(jsc); final HazelcastJavaRDD<Object, Object> mapRdd = hsc.fromHazelcastMap("movie"); final HazelcastJavaRDD<Object, Object> cacheRdd = hsc.fromHazelcastCache("my- cache");

34. @gamussa @hazelcast #oraclecode final SparkConf sparkConf = new SparkConf() .set("hazelcast.server.addresses", "localhost") .set("hazelcast.server.groupName", "dev") .set("hazelcast.server.groupPass", "dev-pass") .set("hazelcast.spark.readBatchSize", "5000") .set("hazelcast.spark.writeBatchSize", "5000") .set("hazelcast.spark.valueBatchingEnabled", "true"); final JavaSparkContext jsc = new JavaSparkContext("spark://localhost:7077", "app", sparkConf); final HazelcastSparkContext hsc = new HazelcastSparkContext(jsc); final HazelcastJavaRDD<Object, Object> mapRdd = hsc.fromHazelcastMap("movie"); final HazelcastJavaRDD<Object, Object> cacheRdd = hsc.fromHazelcastCache("my- cache");

35. @gamussa @hazelcast #oraclecode Demo

36. @gamussa @hazelcast #oraclecode LIMITATIONS

37. @gamussa @hazelcast #oraclecode DATA SHOULD NOT BE UPDATED WHILE READING FROM SPARK

38. @gamussa @hazelcast #oraclecode WHY ?

39. @gamussa @hazelcast #oraclecode MAP EXPANSION SHUFFLES THE DATA INSIDE THE BUCKET

40. @gamussa @hazelcast #oraclecode CURSOR DOESN’T POINT TO CORRECT ENTRY ANYMORE, DUPLICATE OR MISSING ENTRIES COULD OCCUR

41. @gamussa @hazelcast #oraclecode github.com/hazelcast/hazelcast-spark

42. @gamussa @hazelcast #oraclecode THANKS! Any questions? You can find me at @gamussa viktor@hazelcast.com

[OracleCode SF] In memory analytics with apache spark and hazelcast

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