Developing a streaming application using

Apache ApexPramod Immaneni

Apache Apex PMC, Architect DataTorrent IncPDC, Oct 29th 2016

Next Gen Stream Data Processing• Data from variety of sources (IoT, Kafka, files, social media etc.)• Unbounded, continuous data streams

ᵒ Batch can be processed as stream (but a stream is not a batch)• (In-memory) Processing with temporal boundaries (windows)• Stateful operations: Aggregation, Rules, … -> Analytics• Results stored to variety of sinks or destinations

ᵒ Streaming application can also serve data with very low latency

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Browser

Web Server

Kafka Input(logs)

Decompress, Parse, Filter

Dimensions Aggregate Kafka

LogsKafka

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Apache Apex• In-memory, distributed stream processing

• Application logic broken into components called operators that run in a distributed fashion across your cluster

• Natural programming model• Unobtrusive Java API to express (custom) logic• Maintain state and metrics in your member variables

• Scalable, high throughput, low latency• Operators can be scaled up or down at runtime according to the load and SLA• Dynamic scaling (elasticity), compute locality

• Fault tolerance & correctness• Automatically recover from node outages without having to reprocess from

beginning• State is preserved, checkpointing, incremental recovery• End-to-end exactly-once

• Operability• System and application metrics, record/visualize data• Dynamic changes

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Apex Platform Overview

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Native Hadoop Integration

• YARN is the resource manager

• HDFS for storing persistent state

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Application Development Model

A Stream is a sequence of data tuplesA typical 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)

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Stream

Output StreamTuple Tuple

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Enriched Stream

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Scalability

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Dynamic Partitioning

• Partitioning change while application is runningᵒ Change number of partitions at runtime based on statsᵒ Determine initial number of partitions dynamically

• Kafka operators scale according to number of kafka partitionsᵒ Supports re-distribution of state when number of partitions changeᵒ API for custom scaler or partitioner

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Fault Tolerance• Operator state is checkpointed to persistent store

ᵒ Automatically performed by engine, no additional coding neededᵒ Asynchronous and distributed ᵒ In case of failure operators are restarted from checkpoint state

• Automatic detection and recovery of failed containersᵒ Heartbeat mechanismᵒ YARN process status notification

• Buffering to enable replay of data from recovered pointᵒ Fast, incremental recovery, spike handling

• Application master state checkpointedᵒ Snapshot of physical (and logical) planᵒ Execution layer change log

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Checkpointing

Application window Sliding window and tumbling window

Checkpoint window No artificial latency

• In-memory PubSub• Stores results emitted by operator until committed• Handles backpressure / spillover to local disk• Ordering, idempotency

Operator 1

Container 1

BufferServer

Node 1

Operator 2

Container 2

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Buffer Server

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End-to-End Exactly Once

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• Important when writing to external systems• Data should not be duplicated or lost in the external system in case

of application failures• Common external systems

ᵒ Databasesᵒ Filesᵒ Message queues

• Exactly-once = at-least-once + idempotency + consistent state• Data duplication must be avoided when data is replayed from

checkpointᵒ Operators implement the logic dependent on the external systemᵒ Platform provides checkpointing and repeatable windowing

Example application – Streaming WordCount

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• Kafka to Mysql• Streaming source• Traditional database output• Functionality

- Stream messages that contain lines of text from kafka, break them into words, drop commonly occurring words like articles, do a running count of how many times each word occurs and keep updating the totals into a database table

• Five operators• Kafka Input to stream messages from Kafka• Parser to break lines into words• Filter to drop the articles• Counter to count occurrence of each word• Database output to write counts to database

DAG

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KafkaInput Parser Word

CounterDatabaseOutput

CountsWordsLinesKafka Database

Apex Application

• Design and develop operators or use existing ones from the library• Connect operators to form an Application• Configure operators• Configure scaling and other platform attributes• Test functionality, performance and iterate

Filter

Filtered

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Kafka Input

1 to 1 partition 1 to N partition

• Available in Apex Malhar library - KafkaSinglePortStringInputOperator• Operator dynamically scales with partitions of Kafka side• Fault tolerant and idempotent, keeps track of offset for idempotent replay

during failure recovery

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Parser Operator

• Simple parser implementation, splits strings based on a regex pattern• Define input port to receive data and an output port to output data• The callback process is called whenever data is available• To send data operator calls the emit method

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Filter

• Removes articles

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Word Counter

• Simple implementation that keeps the counts in a HashMap in memory• Counts are automatically saved and restored during failure recovery• Periodically emits counts at the end of every window• Unifier not shown here, available in the Apex Malhar library

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Database Output

• Operator in library abstracts the low level logic to communicate with database • Only need to specify the SQL statement and how to populate it with data

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Application

• Instantiate operators and connect operators by connecting the respective ports• Give friendly names to operators and streams

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Configuration

• Use friendly names to specify properties of the operator

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Attributes

• Attributes are platform features and apply to all operators• Scaling, memory, checkpointing etc can be configured using attributes

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Attributes

• Memory can be configured on per operator level or globally• Locality controls how operators are deployed on the cluster

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Runtime topology

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Higher level Application specificationJava Stream API (declarative)

Next Release (3.5): Support for Windowing à la Apache Beam (incubating):@ApplicationAnnotation(name = "WordCountStreamingApiDemo")public class ApplicationWithStreamAPI implements StreamingApplication{ @Override public void populateDAG(DAG dag, Configuration configuration) { String localFolder = "./src/test/resources/data"; ApexStream<String> stream = StreamFactory .fromFolder(localFolder) .flatMap(new Split()) .window(new WindowOption.GlobalWindow(), new TriggerOption().withEarlyFiringsAtEvery(Duration.millis(1000)).accumulatingFiredPanes()) .countByKey(new ConvertToKeyVal()).print(); stream.populateDag(dag); }}

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Operator Library RDBMS• Vertica• MySQL• Oracle• JDBC

NoSQL• Cassandra, Hbase• Aerospike, Accumulo• Couchbase/ CouchDB• Redis, MongoDB• Geode

Messaging• Kafka• Solace• Flume, ActiveMQ• Kinesis, NiFi

File Systems• HDFS/ Hive• NFS• S3

Parsers• XML • JSON• CSV• Avro• Parquet

Transformations• Filters• Rules• Expression• Dedup• Enrich

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

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Monitoring ConsoleLogical View Physical View

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Real-Time Dashboards

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Application Designer

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Maximize Revenue w/ real-time insightsPubMatic is the leading marketing automation software company for publishers. Through real-time analytics, yield management, and workflow automation, PubMatic enables publishers to make smarter inventory decisions and improve revenue performance

Business Need Apex based Solution Client Outcome

• Ingest and analyze high volume clicks & views in real-time to help customers improve revenue

- 200K events/second data flow

• Report critical metrics for campaign monetization from auction and client logs

- 22 TB/day data generated• Handle ever increasing traffic with

efficient resource utilization• Always-on ad network

• DataTorrent Enterprise platform, powered by Apache Apex

• In-memory stream processing• Comprehensive library of pre-built

operators including connectors• Built-in fault tolerance• Dynamically scalable• Management UI & Data Visualization

console

• Helps PubMatic deliver ad performance insights to publishers and advertisers in real-time instead of 5+ hours

• Helps Publishers visualize campaign performance and adjust ad inventory in real-time to maximize their revenue

• Enables PubMatic reduce OPEX with efficient compute resource utilization

• Built-in fault tolerance ensures customers can always access ad network

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Industrial IoT applicationsGE is dedicated to providing advanced IoT analytics solutions to thousands of customers who are using their devices and sensors across different verticals. GE has built a sophisticated analytics platform, Predix, to help its customers develop and execute Industrial IoT applications and gain real-time insights as well as actions.

Business Need Apex based Solution Client Outcome

• Ingest and analyze high-volume, high speed data from thousands of devices, sensors per customer in real-time without data loss

• Predictive analytics to reduce costly maintenance and improve customer service

• Unified monitoring of all connected sensors and devices to minimize disruptions

• Fast application development cycle• High scalability to meet changing business

and application workloads

• Ingestion application using DataTorrent Enterprise platform

• Powered by Apache Apex• In-memory stream processing• Built-in fault tolerance• Dynamic scalability• Comprehensive library of pre-built

operators• Management UI console

• Helps GE improve performance and lower cost by enabling real-time Big Data analytics

• Helps GE detect possible failures and minimize unplanned downtimes with centralized management & monitoring of devices

• Enables faster innovation with short application development cycle

• No data loss and 24x7 availability of applications

• Helps GE adjust to scalability needs with auto-scaling

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Smart energy applications

Silver Spring Networks helps global utilities and cities connect, optimize, and manage smart energy and smart city infrastructure. Silver Spring Networks receives data from over 22 million connected devices, conducts 2 million remote operations per year

Business Need Apex based Solution Client Outcome

• Ingest high-volume, high speed data from millions of devices & sensors in real-time without data loss

• Make data accessible to applications without delay to improve customer service

• Capture & analyze historical data to understand & improve grid operations

• Reduce the cost, time, and pain of integrating with 3rd party apps

• Centralized management of software & operations

• DataTorrent Enterprise platform, powered by Apache Apex

• In-memory stream processing• Pre-built operator • Built-in fault tolerance• Dynamically scalable• Management UI console

• Helps Silver Spring Networks ingest & analyze data in real-time for effective load management & customer service

• Helps Silver Spring Networks detect possible failures and reduce outages with centralized management & monitoring of devices

• Enables fast application development for faster time to market

• Helps Silver Spring Networks scale with easy to partition operators

• Automatic recovery from failures

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Resources for the use cases• Pubmatic

• https://www.youtube.com/watch?v=JSXpgfQFcU8

• GE• https://www.youtube.com/watch?v=hmaSkXhHNu0• http://

www.slideshare.net/ApacheApex/ge-iot-predix-time-series-data-ingestion-service-using-apache-apex-hadoop

• SilverSpring Networks• https://www.youtube.com/watch?v=8VORISKeSjI• http://www.slideshare.net/ApacheApex/iot-big-data-ingestion-and-processing-in-

hadoop-by-silver-spring-networks

Resources

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• Apache Apex - http://apex.apache.org/• Subscribe - http://apex.apache.org/community.html• Download - https://www.datatorrent.com/download/• Twitter

ᵒ @ApacheApex; Follow - https://twitter.com/apacheapexᵒ @DataTorrent; Follow – https://twitter.com/datatorrent

• Meetups - http://www.meetup.com/topics/apache-apex• Webinars - https://www.datatorrent.com/webinars/• Videos - https://www.youtube.com/user/DataTorrent• Slides - http://www.slideshare.net/DataTorrent/presentations • Startup Accelerator Program - Full featured enterprise product

ᵒ https://www.datatorrent.com/product/startup-accelerator/

We Are Hiring

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• jobs@datatorrent.com• Developers/Architects• QA Automation Developers• Information Developers• Build and Release• Community Leaders

Q&A

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APPENDIX

Exactly Once - Files

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File Data

Offset

• Operator saves file offset during checkpoint

• File contents are flushed before checkpoint to ensure there is no pending data in buffer

• On recovery platform restores the file offset value from checkpoint

• Operator truncates the file to the offset

• Starts writing data again• Ensures no data is duplicated or lost

Chk

Exactly Once - Databases

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Data TableMeta Table

• Data in a window is written out in a single transaction

• Window id is also written to a meta table as part of the same transaction

• Operator reads the window id from meta table on recovery

• Ignores data for windows less than the recovered window id and writes new data

• Partial window data before failure will not appear in data table as transaction was not committed

• Assumes idempotency for replay

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Ingestion Solution• Application package with operators ready to use for ingestion• Input and output connectors

• Kafka – Dynamically scalable with Kafka scale• HDFS Block and file• S3• Databases with JDBC

- Postgres, Mysql• Processing

• Deduper• Parsing, Filtering & Transform

• Comes with pre-built pipelines• Kafka to HDFS with Deduper• HDFS sync between two clusters or S3 to HDFS

• Currently in beta• If interested please contact info@datatorrent.com