@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

@gamussa @hazelcast #oraclecode RDD

@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

@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

@gamussa @hazelcast #oraclecode

@gamussa @hazelcast #oraclecode RDD Actions

@gamussa @hazelcast #oraclecode

@gamussa @hazelcast #oraclecode

@gamussa @hazelcast #oraclecode RDD Fault Tolerance

@gamussa @hazelcast #oraclecode

@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

@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 mapRdd = hsc.fromHazelcastMap("movie"); final HazelcastJavaRDD cacheRdd = hsc.fromHazelcastCache("my- cache");

@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 mapRdd = hsc.fromHazelcastMap("movie"); final HazelcastJavaRDD cacheRdd = hsc.fromHazelcastCache("my- cache");

@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 mapRdd = hsc.fromHazelcastMap("movie"); final HazelcastJavaRDD cacheRdd = hsc.fromHazelcastCache("my- cache");

@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 mapRdd = hsc.fromHazelcastMap("movie"); final HazelcastJavaRDD cacheRdd = hsc.fromHazelcastCache("my- cache");

@gamussa @hazelcast #oraclecode Demo

@gamussa @hazelcast #oraclecode LIMITATIONS

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

@gamussa @hazelcast #oraclecode WHY ?

@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 [email protected]