Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra Tagare

Actionable Insights with Apache Apex at Apache Big Data 2017 by Devendra Tagare

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  Devendra Tagare <devtagare@gmail.com> DataEngineer @ DataTorrent Inc Committer @ Apache Software Foundation for Apex @devtagare ApacheCon North America, 2017 Dimensions Computation With Apache Apex
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  What is Apex? 2 ✓ Platform and Runtime Engine - enables development of scalable and fault-tolerant distributed applications for processing streaming and batch data ✓ Highly Scalable - Scales linearly to billions of events per second with statically defined or dynamic partitioning, advanced locality & affinity ✓ Highly Performant - In memory computations.Can reach single digit millisecond end-to-end latency ✓ Fault Tolerant - Automatically recovers from failures - without manual intervention ✓ Stateful - Guarantees that no state will be lost ✓ YARN Native - Uses Hadoop YARN framework for resource negotiation ✓ Developer Friendly - Exposes an easy API for developing Operators, which can include any custom business logic written in Java, and provides a Malhar library of many popular operators and application examples.High level API for data scientists/ analysts.
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  Apex In theWild Data Sources Op1 Hadoop (YARN + HDFS) Real-time Analytics & Visualizations Op3 Op2 Op4 Streaming Computation Actions & Insights Data Targets
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  44 The Apex Ecosystem Solutionsfor Business Ingestion & Data Prep ETL Pipelines 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 API Transformation ML & Score SQL Analytics FileSync Dev Framework Batch Support Apache Apex Core Kafka-to-HDFS JDBC-to-HDFS HDFS-to-HDFS S3-to-HDFS
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  Application Development Model 5 ●A Stream is a sequence of data tuples ● A 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 Stream Tuple Tuple FilteredStream Enriched Stream Enriched Stream er Operator er Operator er Operator er Operator er Operator er Operator
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  Stream Locality • Bydefault operators are deployed in containers (processes) randomly on different nodes across the Hadoop cluster • Custom locality for streams Rack local: Data does not traverse network switches Node local: Data is passed via loopback interface and frees up network bandwidth Container local: Messages are passed via in memory queues between operators and does not require serialization Thread local: Messages are passed between operators in a same thread equivalent to calling a subsequent function on the message
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  Fault Tolerance • Operatorstate is check-pointed to a persistent store Automatically performed by engine, no additional work needed by operator In case of failure operators are restarted from checkpoint state Frequency configurable per operator Asynchronous and distributed by default Default store is HDFS • Automatic detection and recovery of failed operators Heartbeat mechanism • Buffering mechanism to ensure replay of data from recovered point so that there is no loss of data • Application master state check-pointed
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  Processing Guarantees At-least once •On recovery data will be replayed from a previous checkpoint Messages will not be lost Default mechanism and is suitable for most applications • Can be used in conjunction with following mechanisms to achieve exactly-once behavior in fault recovery scenarios Transactions with meta information, Rewinding output, Feedback from external entity, Idempotent operations At-most once • On recovery the latest data is made available to operator Useful in use cases where some data loss is acceptable and latest data is sufficient Exactly once • At least once + state recovery + operator logic to achieve end-to-end exactly once
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  Apex Operator API InputAdapters - read from external systems & emit tuples to downstream operators, no input port Generic Operators - process incoming data received from input adapters or other generic operators.Have both input & output ports Output Adapters - write to external systems, no output ports
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  Dimensions Compute ReferenceArchitecture Kafka/ HDFS Parser Parser Parser Enrich & Transform Enrich & Transform Enrich & Transform Dimensional Compute Dimensional Compute Dimensional Compute Store Query-In Results Visualization Input Tuples Input Tuples Input Tuples Parsed Tuples Parsed Tuples Parsed Tuples Enriched Tuples Enriched Tuples Enriched Tuples Aggregates Aggregates Aggregates Visualization Results Visualization Query Aggregate Query Aggregate Results
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  Dimensional Model -Key Concepts Metrics : pieces of information we want to collect statistics about. Dimensions : variables which can impact our measures. Combinations : set of dimensions for which one or metric would be aggregated.They are sub-sets of dimensions. Aggregations : the aggregate function eg.. SUM, TOPN, Standard deviation. Time Buckets : Time buckets are windows of time. Aggregations for a time bucket are comprised only of events with a time stamp that falls into that time bucket. With the managed state and High level api - Windowed operations also supported for fix window, sliding window, session window for event time, system time, ingestion time. Example : Ad-Tech : aggregate over key dimensions for revenue metrics Dimensions - campaignId, advertiserId, time Metrics - Cost, revenue, clicks, impressions Aggregate functions -SUM,AM etc.. Combinations : 1. campaignId x time - cost,revenue 2. advertiser - revenue, impressions 3. campaignId x advertiser x time - revenue, clicks, impressions 11
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  Phases of DimensionalCompute Aggregations in reality….. 12 Why break dimensional compute into stages ? Aggregate footprint in memory generally rises exponentially over time Scalable implementations of dimensions compute need to handle 100K+ event/sec. Phases of dimensions compute The pre-aggregation phase The unification phase The aggregation storage phase
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  Unique Aggregates :Dimensions Computation to scale by reducing the number of events entering the system Example : ‘n’ events flowing through the system actually translate to a lower # unique aggregates eg 500,000 adEvents flowing through the system actually translate to around 10,000 aggregates due to repeating keys. Partitioning : use partitioning to scale up the dimensional compute. Example : If a partition can handle 500,000 events/second, then 8 partitions would be able to handle 4,000,000 events/second which are effectively combined into 80,00 aggregates/second Problem of the Incomplete Aggregations ? Aggregate values from previous batches not factored in - corrected in the Aggregation Storage phase. Different partitions may share the say key and time buckets - partial aggregates - corrected in Unification phase. Setting up the Pre-Aggregation phase of Dimensions Computation involves configuring a Dimension Computation operator - DimensionsComputationFlexibleSingleSchemaPOJO The Pre-aggregation phase 13
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  Ad Event public AdEvent(StringpublisherId, String campaignId String location, double cost, double revenue, long impressions, long clicks, long time….) { this.publisherId = publisherId; this.campaignId = campaignId; this.location = location; this.cost = cost; this.revenue = revenue; this.impressions = impressions; this.clicks = clicks; this.time = time; …. } /* Getters and setters go here */ {"keys":[{"name":"campaignId","type":"integer"}, {"name":"adId","type":"integer"}, {"name":"creativeId","type":"integer"}, {"name":"publisherId","type":"integer"}, {"name":"adOrderId","type":"integer"}], "timeBuckets":["1h","1d"], "values": [{"name":"impressions","type":"integer","aggregators":["SUM"]} , {"name":"clicks","type":"integer","aggregators":["SUM"]}, {"name":"revenue","type":"integer"}], "dimensions": [{"combination":["campaignId","adId"]}, {"combination":["creativeId","campaignId"]}, {"combination":["campaignId"]}, {"combination":["publisherId","adOrderId","campaignId"], "additionalValues":["revenue:SUM"]}] } The Dimensional Model 14
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  Combines outputs -combines the outputs of all the partitions in the Pre-Aggregation phase into a single single stream which can be passed on to the storage phase Why combine ? To reduce the number of aggregations even further ~ lower memory footprint, higher throughput This is because the aggregations produced by different partitions which share the same key and time bucket can be combined to produce a single aggregation ~ completeness for point to point query Example : if the Unification phase receives 80,000 aggregations/second, you can expect 20,000 aggregations/second after unification. Implementation : Add a unifier that can be set on your dimensions computation operator, dimensions.setUnifier(new DimensionsComputationUnifierImpl<InputEvent, Aggregate>()); The Unification Phase 15
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  Aggregation Persistence :Aggregations are persisted to HDFS using HDHT. Dimensions Store persists aggregates and serves the below functions Functions as a storage so that aggregations can be retrieved for visualization. Functions as a storage allowing aggregations to be combined with incomplete aggregates produced by Unification. Visualization The Dimensions Store allows you to visualize your aggregations over time. This is done by allowing queries and responses to be received from and sent to the UI via websocket. Aggregation The store produces complete aggregations by combining the incomplete aggregations received from the Unification stage with aggregations persisted to HDFS. Why have the previous phases ? Dimensions Store is I/O intensive, and may cause bottle-necks. Previous phases reduce the cardinality of events so that the Store will always have lesser # events. Other variants & the new way : Use managed state instead of HDHT. The Aggregation Storage Phase 16
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  Visualization with Apex 17AdEvents over time Query Browser creates a websocket connection with the pubsub server hosted by a webserver. UI Widgets based on the Malhar angular dashboard can send queries to the pubsub server via this connection to a specific topic. These queries are parsed by the Query operator and passed onto DimensionsStore to fetch data from HDHT Store. QueryResult The QueryResult operator gets the result from the DimensionsStore operator for a given query, formats and renders it to the widget.
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  Sample Visualization
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  Q & A ThankYou !!!
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  Resources Apache Apex -http://apex.apache.org/ References : http://docs.datatorrent.com/ Subscribe to forums : Apex - http://apex.apache.org/community.html Download - http://apex.apache.org/downloads Twitter : @ApacheApex; Follow - https://twitter.com/apacheapex Meetups - http://meetup.com/topics/apache-apex 20