Introduction to Apache Apex

Pramod ImmaneniPMC Apex & Chief Architect,

DTpramod@datatorrent.com

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Stream Processing

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

•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

•Streaming application can also serve data with very low latency

Browser

Web Server

Kafka Input(logs)

Decompress, Parse, Filter

Dimensions Aggregate Kafka

LogsKafka

Apache Apex

• Stream processing platform• In memory, distributed

• Simple programming model• Write your own custom logic, pipelining

• Scalable• High throughput, low latency• Dynamic scaling responding to SLA

• Fault tolerant• Node outages, hadoop outages• Stateful recovery, incremental recovery• End to end exactly once

• Productivity library• Commonly needed connectors, business logic• Production tested

• Operability – DataTorrent RTS• Deployment and monitoring console• Deep introspection and debugging

Apache Apex and DataTorrent Product StackDesigned to help you at every stage of your data-in-motion pipeline

Solutions for Business Problems

Ingestion & Data Prep ETL Pipelines

Ease of Use Tools Real-Time Data VisualizationManagement & MonitoringGUI Application

Assembly

Application Templates

Apex-Malhar Operator Library

Big Data Infrastructure Hadoop 2.x – YARN + HDFS – On Prem & Cloud

Core

High-level APITransformation ML & Score SQL Analytic

s

FileSync

Dev Framework

Batch Support

Apache Apex Core

Kafka HDFS

HDFS HDFS JDBC HDFS JDBC

Kafka

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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)

Filtered

Stream

Output StreamTuple Tuple

Filtered Stream

Enriched Stream

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Native Hadoop Integration

• YARN is the resource manager

• HDFS for storing persistent state

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Sample Application

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

Apex Malhar - Production Ready Operators

RDBMS• Vertica• MySQL• Oracle• JDBC

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

Messaging• Kafka• Solace• Flume• ActiveMQ• Kinesis

File Systems• HDFS/ Hive• NFS• S3

Parsers• XML • JSON• CSV• Avro• Parquet

Transformations• Filters• Rules• Expression• Dedup• Enrich

Analytics• Dimensional Aggregations

Protocols• HTTP• FTP• WebSocket• MQTT• SMTP

Other• Elastic Search• Script (JavaScript, Python,

R)• Geode• Nifi• Twitter

Operator Maturity Framework

Scalability and Idempotency

Documentation

Example Applications

Internal Certification

Benchmarking

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Filter Operator

• Removes articles

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Application Definition

Instantiate operators Connect operators by connecting output port to input port

• Multiple ways• Compositional, High Level with Windowing, Beam, Stream SQL, JSON, Property file

• Compositional example

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Scalability

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Partitioning & Unification

Cascade Unifier

Parallel Partitioning

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Dynamic Scaling

• 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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Runtime topology

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Fault Tolerance & 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

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Buffer Server

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End-to-End Exactly Once

•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

•Databases•Files•Message queues

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

•Operators implement the logic dependent on the external system•Platform provides checkpointing and repeatable windowing

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Monitoring Console•Logical View Physical View

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Real-Time Dashboards

Application Designer

AppHub – App Template Repository

• Application repository that serves demos and applications that are backed by DataTorrent

• Provides version updates via dtManage

• Future Opportunities• Apps on Cloud Big Data

environments such as Azure, EMR

• Marketplace for applications created by the community

GE powers Industrial IoT applications with DataTorrent

18 | © 2016 DataTorrent Confidential – Do Not Distribute

GE 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 DataTorrent 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

• Built an 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 including connectors

• Management UI console

• Helps GE improve performance and lower costs

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

• Enables faster innovation with short application development cycle

• No data loss and 24x7 availability of applications

• Helps GE easily adjust to scalability needs with auto-scaling

CapitalOne prevents fraudulent credit card transactions in real-time with DataTorrent

18 | © 2016 DataTorrent Confidential – Do Not Distribute

Capital One is the eighth-largest bank holding company in the US and specializes in credit cards, home loans, auto loans, banking and savings products. It is using Apache Apex to build their next generation decisioning platform, achieving an ultra-low latency of under 2 ms for decision making, and handling 2,000 events burst at a net rate of 70,000 events/sec.

Business Need DataTorrent Solution• Replaces existing mainframe system

for CapitalOne

• No data loss and 24x7 availability of applications

• Easy integration with the existing infrastructure HDFS, messaging queues & in-memory data grids

• Achieved 0.25 ms latency (on average), 2 ms latency (99.99%), 16 ms (99.9999).

Client Outcome• Decline fraudulent credit card

transactions• Open Source • Decision to authorize or decline a

transaction in real time - SLA is 40 ms (ideally 15 ms)

• Process 2,000 events/ 10ms• Transactions may be declined based

on • a transaction attribute or several

attributes &• dynamically changing rules (e.g.

an account is disabled or an account limit is reached)

• DataTorrent 2.0 Enterprise platform, powered by Apache Apex

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

built operators including connectors

• The only enterprise ready solution

• Built-in fault tolerance• Dynamically scalable• Use precomputed ML model for

scoring• Management UI & Data

Visualization console

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Resources for the use cases

•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

•Capitol One•https://www.youtube.com/watch?v=98EW5NGM3u0•http://www.slideshare.net/ApacheApex/capital-ones-next-generation-decision-in-less-than-2-ms

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Resources• http://apex.apache.org/• Learn more: http://apex.apache.org/docs.html • Subscribe - http://apex.apache.org/community.html• Download - http://apex.apache.org/downloads.html• Follow @ApacheApex - https://twitter.com/apacheapex• Meetups – http://www.meetup.com/pro/apacheapex/• More examples: https://github.com/DataTorrent/examples• Slideshare:

http://www.slideshare.net/ApacheApex/presentations• https://www.youtube.com/results?search_query=apache+ape

x• Free Enterprise License for Startups -

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

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Q&A

Thank you & enjoy the conference

EXTRA

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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

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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d11 d12 d13

d21 d22 d23

lwn1 lwn2 lwn3

op-id wn

chk wn wn+1

Lwn+11 Lwn+12 Lwn+13

op-id wn+1

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

Application Specification (Java)

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Java Stream API (declarative)

DAG API (compositional)

Java Streams API + Windowing

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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); }}

Writing an Operator

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Example application - WordCount

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

- Stream messages containing string lines- Break the lines into words on space and punctuation boundaries- Count unique words- Store counts in database

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

Attributes

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• Attributes are platform features and apply to all operators