BigDataSpain 2016: Stream Processing Applications with Apache Apex

Building Streaming Applications with Apache Apex Thomas Weise <thw@apache.org> @thweise PMC Chair Apache Apex, Architect DataTorrent Big Data Spain, Madrid, Nov 18th 2016

Agenda 2 • Application Development Model • Creating Apex Application - Project Structure • Application Developer API • Configuration Example • Operator Developer API • Overview of Operator Library • Frequently used Connectors • Stateful Transformation & Windowing • Scalability – Partitioning • End-to-end Exactly Once

Application Development Model 3 ▪Stream is a sequence of data tuples ▪Operator takes one or more input streams, performs computations & emits one or more output streams • Each Operator is YOUR custom business logic in java, or built-in operator from our open source library • Operator has many instances that run in parallel and each instance is single-threaded ▪Directed Acyclic Graph (DAG) is made up of operators and streams Directed Acyclic Graph (DAG) Output Stream Tupl e Tupl e er Operator er Operator er Operator er Operator er Operator er Operator

Creating Apex Application Project 4 chinmay@chinmay-VirtualBox:~/src$ mvn archetype:generate -DarchetypeGroupId=org.apache.apex - DarchetypeArtifactId=apex-app-archetype -DarchetypeVersion=LATEST -DgroupId=com.example - Dpackage=com.example.myapexapp -DartifactId=myapexapp -Dversion=1.0-SNAPSHOT … … ... Confirm properties configuration: groupId: com.example artifactId: myapexapp version: 1.0-SNAPSHOT package: com.example.myapexapp archetypeVersion: LATEST Y: : Y … … ... [INFO] project created from Archetype in dir: /media/sf_workspace/src/myapexapp [INFO] ------------------------------------------------------------------------ [INFO] BUILD SUCCESS [INFO] ------------------------------------------------------------------------ [INFO] Total time: 13.141 s [INFO] Finished at: 2016-11-15T14:06:56+05:30 [INFO] Final Memory: 18M/216M [INFO] ------------------------------------------------------------------------ chinmay@chinmay-VirtualBox:~/src$ https://www.youtube.com/watch?v=z-eeh-tjQrc

Apex Application Project Structure 5 • pom.xml •Defines project structure and dependencies •Application.java •Defines the DAG •RandomNumberGenerator.java •Sample Operator •properties.xml •Contains operator and application properties and attributes •ApplicationTest.java •Sample test to test application in local mode

API: Compositional (low level) 6 Input Parser Counter Output CountsWordsLines Kafka Database Filter Filtered

API: Declarative (high level) 7 File Input Parser Word Counter Console Output CountsWordsLines Folder StdOut StreamFactory.fromFolder("/tmp") .flatMap(input -> Arrays.asList(input.split(" ")), name("Words")) .window(new WindowOption.GlobalWindow(), new TriggerOption().accumulatingFiredPanes().withEarlyFiringsAtEvery(1)) .countByKey(input -> new Tuple.PlainTuple<>(new KeyValPair<>(input, 1L)), name("countByKey")) .map(input -> input.getValue(), name("Counts")) .print(name("Console")) .populateDag(dag);

API: SQL 8 Kafka Input CSV Parser Filter CSV Formattter FilteredWordsLines Kafka File Project Projected Line Writer Formatted SQLExecEnvironment.getEnvironment() .registerTable("ORDERS", new KafkaEndpoint(conf.get("broker"), conf.get("topic"), new CSVMessageFormat(conf.get("schemaInDef")))) .registerTable("SALES", new FileEndpoint(conf.get("destFolder"), conf.get("destFileName"), new CSVMessageFormat(conf.get("schemaOutDef")))) .registerFunction("APEXCONCAT", this.getClass(), "apex_concat_str") .executeSQL(dag, "INSERT INTO SALES " + "SELECT STREAM ROWTIME, FLOOR(ROWTIME TO DAY), APEXCONCAT('OILPAINT', SUBSTRING(PRODUCT, 6, 7) " + "FROM ORDERS WHERE ID > 3 AND PRODUCT LIKE 'paint%'");

API: Beam 9 • Apex Runner for Apache Beam is now available!! •Build once run-anywhere model •Beam Streaming applications can be run on apex runner: public static void main(String[] args) { Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class); // Run with Apex runner options.setRunner(ApexRunner.class); Pipeline p = Pipeline.create(options); p.apply("ReadLines", TextIO.Read.from(options.getInput())) .apply(new CountWords()) .apply(MapElements.via(new FormatAsTextFn())) .apply("WriteCounts", TextIO.Write.to(options.getOutput())); .run().waitUntilFinish(); }

API: SAMOA 10 •Build once run-anywhere model for online machine learning algorithms •Any machine learning algorithm present in SAMOA can be run directly on Apex. •Uses Apex Iteration Support •Following example does classification of input data from HDFS using VHT algorithm on Apex: ᵒ bin/samoa apex ../SAMOA-Apex-0.4.0-incubating-SNAPSHOT.jar "PrequentialEvaluation -d /tmp/dump.csv -l (classifiers.trees.VerticalHoeffdingTree -p 1) -s (org.apache.samoa.streams.ArffFileStream -s HDFSFileStreamSource -f /tmp/user/input/covtypeNorm.arff)"

Configuration (properties.xml) 11 Input Parser Counter Output CountsWordsLines Kafka Database Filter Filtered

Streaming Window Processing Time 12 •Finite time sliced windows based on processing (event arrival) time •Used for bookkeeping of streaming application •Derived Windows are: Checkpoint Windows, Committed Windows

Operator API 13 Next streaming window Next streaming window Input Adapters - Starting of the pipeline. Interacts with external system to generate stream Generic Operators - Processing part of pipeline Output Adapters - Last operator in pipeline. Interacts with external system to finalize the processed stream OutputPort::emit()

Operator Library 14 RDBMS • JDBC • MySQL • Oracle • MemSQL NoSQL • Cassandra, HBase • Aerospike, Accumulo • Couchbase/ CouchDB • Redis, MongoDB • Geode Messaging • Kafka • JMS (ActiveMQ etc.) • Kinesis, SQS • Flume, NiFi File Systems • HDFS/ Hive • Local File • S3 Parsers • XML • JSON • CSV • Avro • Parquet Transformations • Filters, Expression, Enrich • Windowing, Aggregation • Join • Dedup Analytics • Dimensional Aggregations (with state management for historical data + query) Protocols • HTTP • FTP • WebSocket • MQTT • SMTP Other • Elastic Search • Script (JavaScript, Python, R) • Solr • Twitter

Frequently used Connectors Kafka Input 15 KafkaSinglePortInputOperator KafkaSinglePortByteArrayInputOperator Library malhar-contrib malhar-kafka Kafka Consumer 0.8 0.9 Emit Type byte[] byte[] Fault-Tolerance At Least Once, Exactly Once At Least Once, Exactly Once Scalability Static and Dynamic (with Kafka metadata) Static and Dynamic (with Kafka metadata) Multi-Cluster/Topic Yes Yes Idempotent Yes Yes Partition Strategy 1:1, 1:M 1:1, 1:M

Frequently used Connectors Kafka Output 16 KafkaSinglePortOutputOperator KafkaSinglePortExactlyOnceOutputOperator Library malhar-contrib malhar-kafka Kafka Producer 0.8 0.9 Fault-Tolerance At Least Once At Least Once, Exactly Once Scalability Static and Dynamic (with Kafka metadata) Static and Dynamic, Automatic Partitioning based on Kafka metadata Multi-Cluster/Topic Yes Yes Idempotent Yes Yes Partition Strategy 1:1, 1:M 1:1, 1:M

Frequently used Connectors File Input 17 •AbstractFileInputOperator •Used to read a file from source and emit the content of the file to downstream operator •Operator is idempotent •Supports Partitioning •Concrete classes: •FileLineInputOperator •AvroFileInputOperator •ParquetFilePOJOReader •https://www.datatorrent.com/blog/f ault-tolerant-file-processing/

Frequently used Connectors File Output 18 •AbstractFileOutputOperator •Writes data to a file •Supports Partitions •Exactly-once results •Upstream operators should be idempotent •Concrete classes: •StringFileOutputOperator •https://www.datatorrent.com/blog/f ault-tolerant-file-processing/

Windowing Support 19 • Event-time Windows • Computation based on event-time present in the tuple • Types of windows: ᵒ Global : Single event-time window throughout the lifecycle of application ᵒ Timed : Tuple is assigned to single, non-overlapping, fixed width windows immediately followed by next window ᵒ Sliding Time : Tuple is can be assigned to multiple, overlapping fixed width windows. ᵒ Session : Tuple is assigned to single, variable width windows with predefined min gap

Stateful Windowed Processing 20 • WindowedOperator (org.apache.apex:malhar-library) • Used to process data based on Event time as contrary to ingression time • Supports windowing semantics of Apache Beam model • Features: ᵒ Watermarks ᵒ Allowed Lateness ᵒ Accumulation ᵒ Accumulation Modes: Accumulating, Discarding, Accumulating & Retracting ᵒ Triggers • Storage ᵒ In memory (checkpointed) ᵒ Managed State

Stateful Windowed Processing Compositional API 21 @Override public void populateDAG(DAG dag, Configuration configuration) { WordGenerator inputOperator = new WordGenerator(); KeyedWindowedOperatorImpl windowedOperator = new KeyedWindowedOperatorImpl(); Accumulation<Long, MutableLong, Long> sum = new SumAccumulation(); windowedOperator.setAccumulation(sum); windowedOperator.setDataStorage(new InMemoryWindowedKeyedStorage<String, MutableLong>()); windowedOperator.setRetractionStorage(new InMemoryWindowedKeyedStorage<String, Long>()); windowedOperator.setWindowStateStorage(new InMemoryWindowedStorage<WindowState>()); windowedOperator.setWindowOption(new WindowOption.TimeWindows(Duration.standardMinutes(1))); windowedOperator.setTriggerOption(TriggerOption.AtWatermark() .withEarlyFiringsAtEvery(Duration.millis(1000)) .accumulatingAndRetractingFiredPanes()); windowedOperator.setAllowedLateness(Duration.millis(14000)); ConsoleOutputOperator outputOperator = new ConsoleOutputOperator(); dag.addOperator("inputOperator", inputOperator); dag.addOperator("windowedOperator", windowedOperator); dag.addOperator("outputOperator", outputOperator); dag.addStream("input_windowed", inputOperator.output, windowedOperator.input); dag.addStream("windowed_output", windowedOperator.output, outputOperator.input); }

Stateful Windowed Processing Declarative API 22 StreamFactory.fromFolder("/tmp") .flatMap(input -> Arrays.asList(input.split(" ")), name("ExtractWords")) .map(input -> new TimestampedTuple<>(System.currentTimeMillis(), input), name("AddTimestampFn")) .window(new TimeWindows(Duration.standardMinutes(WINDOW_SIZE)), new TriggerOption().accumulatingFiredPanes().withEarlyFiringsAtEvery(1)) .countByKey(input -> new TimestampedTuple<>(input.getTimestamp(), new KeyValPair<>(input.getValue(), 1L))), name("countWords")) .map(new FormatAsTableRowFn(), name("FormatAsTableRowFn")) .print(name("console")) .populateDag(dag);

Scalability - Partitioning 23 0 1 2 3 Logical DAG 0 1 2 U Physical DAG 1 1 2 2 3 Parallel Partitions M x N Partitions OR Shuffle <configuration> <property> <name>dt.operator.1.attr.PARTITIONER</name> <value>com.datatorrent.common.partitioner.StatelessPartitioner:3</value> </property> <property> <name>dt.operator.2.port.inputPortName.attr.PARTITION_PARALLEL</name> <value>true</value> </property> </configuration>

How tuples are split between partitions 24 • Tuple hashcode and mask used to determine destination partition ᵒ Mask picks the last n bits of the hashcode of the tuple ᵒ hashcode method can be overridden • StreamCodec can be used to specify custom hashcode for tuples ᵒ Can also be used for specifying custom serialization tuple: { Name, 24204842, San Jose } Hashcode: 0010101000101 01 Mask (0x11) Partition 00 1 01 2 10 3 11 4

End-to-End Exactly-Once 25 Input Counter Store Aggregate CountsWords Kafka Database ● Input ○ Uses com.datatorrent.contrib.kafka.KafkaSinglePortStringInputOperator ○ Emits words as a stream ○ Operator is idempotent ● Counter ○ com.datatorrent.lib.algo.UniqueCounter ● Store ○ Uses CountStoreOperator ○ Inserts into JDBC ○ Exactly-once results (End-To-End Exactly-once = At-least-once + Idempotency + Consistent State) https://github.com/DataTorrent/examples/blob/master/tutorials/exactly-once https://www.datatorrent.com/blog/end-to-end-exactly-once-with-apache-apex/

End-to-End Exactly-Once (contd.) 26 Input Counter Store Aggregate CountsWords Kafka Database public static class CountStoreOperator extends AbstractJdbcTransactionableOutputOperator<KeyValPair<String, Integer>> { public static final String SQL = "MERGE INTO words USING (VALUES ?, ?) I (word, wcount)" + " ON (words.word=I.word)" + " WHEN MATCHED THEN UPDATE SET words.wcount = words.wcount + I.wcount" + " WHEN NOT MATCHED THEN INSERT (word, wcount) VALUES (I.word, I.wcount)"; @Override protected String getUpdateCommand() { return SQL; } @Override protected void setStatementParameters(PreparedStatement statement, KeyValPair<String, Integer> tuple) throws SQLException { statement.setString(1, tuple.getKey()); statement.setInt(2, tuple.getValue()); } }

End-to-End Exactly-Once (contd.) 27 https://www.datatorrent.com/blog/fault-tolerant-file-processing/

Who is using Apex? 28 • Powered by Apex ᵒ http://apex.apache.org/powered-by-apex.html ᵒ Also using Apex? Let us know to be added: users@apex.apache.org or @ApacheApex • Pubmatic ᵒ https://www.youtube.com/watch?v=JSXpgfQFcU8 • GE ᵒ https://www.youtube.com/watch?v=hmaSkXhHNu0 ᵒ http://www.slideshare.net/ApacheApex/ge-iot-predix-time-series-data-ingestion-service- using-apache-apex-hadoop • SilverSpring Networks ᵒ https://www.youtube.com/watch?v=8VORISKeSjI ᵒ http://www.slideshare.net/ApacheApex/iot-big-data-ingestion-and-processing-in-hadoop-by- silver-spring-networks

Resources 29 • http://apex.apache.org/ • Learn more - http://apex.apache.org/docs.html • Getting involved - http://apex.apache.org/community.html • Download - http://apex.apache.org/downloads.html • Follow @ApacheApex - https://twitter.com/apacheapex • Meetups - https://www.meetup.com/topics/apache-apex/ • Examples - https://github.com/DataTorrent/examples • Slideshare - http://www.slideshare.net/ApacheApex/presentations • https://www.youtube.com/results?search_query=apache+apex • Free Enterprise License for Startups - https://www.datatorrent.com/product/startup-accelerator/

BigDataSpain 2016: Stream Processing Applications with Apache Apex

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