Introducing Athena: 08/19 Big Data Application Meetup, Talk #3

Aug 25, 20150 likes964 views

Athena is Uber's platform for stream processing built on Apache Samza. It uses Kafka as the messaging system and allows for near real-time computation of data streams. Some current uses at Uber include pricing calculations, event processing, and driver status updates. Athena provides tooling like a job manager, configuration files, testing frameworks, and integration with services like Graphite. Ongoing work includes improving upgrades, adding more metrics, and enhancing configuration validation.

Athena
Streaming with Samza

Goddess of Wisdom

Brief Background
Stream Processing, Samza, Athena
Kafka Uber uses Kafka as logging/event system
Message streams
Near-real time
computation
Stream Processing Framework:
Apache Samza
Platform built on top of Samza:
Athena

Pricing
Assess supply and demand in real time to calculate
accurate surge multiples
Kafka Samza
location updates
Elasticsearch
S3 Spark
location updates
HTTP
Service
Realtime
Batch
Queries

Mapper
Job
Mapper
Job
Mapper
Job
Event
parsing,
filtering,

classification

Event
Aggregation

Raw
events
Riak
Inserter
Job
Inserter
Job
Tiles
1m Agg
TilesReducer
Job
Reducer
Job
1m Agg
Tiles
Per
Contract
Common

Artemis
Samza

Current use cases
Event driven update engine

Driver Activation
KAFKA SAMZA
Driver status
updates
Onboarding
Service
Fetch
driver
info
RocksDB
Retry queue
Update
status

Fraud monitoring and alerting
KAFKA Partitioner
Real Time
Hourly
Alerting
Service
Monitoring
Service
Uberx metrics

Da Vinci: Streaming platform for data science
KAFKA
Aggregation job
(Model generation)
RocksDB
Historical state
Timeseries
data
Model evaluation
Database /
Elasticsearch
updates
Model
Updates

Introducing Athena: 08/19 Big Data Application Meetup, Talk #3

Tooling
●  Athena manager
●  Job configuration
●  Unit test framework
●  Graphite integration
●  Codahale library support
●  Maven archetype
●  Artifactory support

Job conﬁguration
reference.conf
Sandbox Staging ProductionDev
athena-core-lib
application.conf
Sandbox Staging ProductionDev
Samza job

$Job conﬁguration projects { artemis { job_1 { mapper { envs.common { task.inputs = "kafka.topic_1" task.class = "com.uber.athena.SampleTaskClass" } envs.local = ${projects.artemis.job_1.mapper.envs.common} envs.local = { task.window.ms = 30000 } envs.sandbox = ${projects.artemis.job_1.mapper.envs.local} envs.sandbox = { yarn.package.path = "http://artifactory..../artifactory/libs-snapshot-local/com/uber/athena/.../hello-athena.tar.gz" } envs.staging = ${projects.artemis.job_1.mapper.envs.sandbox} envs.production = ${projects.artemis.job_1.mapper.envs.sandbox} }$

Unit test framework
Stream Job
StreamTask InitableTask WindowableTask
TaskUnitTestHarness
Message Listener
Inject data Custom
IncomingMessageEnvelope
Custom
MessageCollector

Unit test framework
String classTaskName = "com.uber.athena.test.TestProcessTask";
// Register the job to the test harness
TaskUnitTestHarness<String, Integer> testProcessTask = new
TaskUnitTestHarness<>(classTaskName, false, true);
// Register a listener for validating the output of every process function
testProcessTask.registerMessageListenerOnProcess(new KeyAsserterListener());
// Start the job
testProcessTask.start();
// Inject data
testProcessTask.inject(key,value);
// Get the full output
testWindowTask.getResult()

Observations
●  YARN is not bad !
●  Offset lag and Buffered messages
●  Kafka Appender for ELK
●  Checkpoint topic partition incorrect count
●  Config validation needs improvement
●  Job restarts are complicated
●  Built-in Metrics are insufficient

●  Seamless upgrades
●  Custom built-in serde support
●  Config validation enhancement
●  Auto benchmarking a Samza job
●  Unit test framework enhancement
Upcoming Samza improvements

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Speaker: Nitin Motgi, Cask Big Data Applications Meetup, 09/20/2017 Palo Alto, CA More info here: http://www.meetup.com/BigDataApps/ Link to video: https://www.youtube.com/watch?v=FnQwDaKii2U About the talk: Business Rules are statements that describe business policies or procedures to process data. Rules engines or inference engines execute business rules in a runtime production environment, and have become commonplace for many IT applications. Except in the world of big data, where there has been a gap for a horizontally scalable, lightweight inference-based business rules engine for big data processing. In this session, you learn about Cask’s new business Rule Rngine built on top of CDAP, which is a sophisticated if-then-else statement interpreter that runs natively on big data systems such as Spark, Hadoop, Amazon EMR, Azure HDInsight and GCE. It provides an alternative computational model for transforming your data while empowering business users to specify and manage the transformations and policy enforcements. In his talk, Nitin Motgi, Cask co-founder and CTO, demonstrates this new, distributed rule engine and explain how business users in big data environments can make decisions on their data, enforce policies, and be an integral part of the data ingestion and ETL process. He also shows how business users can write, manage, deploy, execute and monitor business data transformation and policy enforcements. Check out http://bdam.io/ for more info on the Big Data Apps meetup!

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Transaction in HBase, by Andreas Neumann, CaskCask Data

Title: Transactions in HBase Speaker: Andreas Neumann, Cask ApacheCon Big Data, Miami, FL May 18, 2017 Abstract: In the age of NoSQL, big data storage engines such as HBase have given up ACID semantics of traditional relational databases, in exchange for high scalability and availability. However, it turns out that in practice, many applications require consistency guarantees to protect data from concurrent modification in a massively parallel environment. In the past few years, several transaction engines have been proposed as add-ons to HBase: Three different engines, namely Omid, Tephra, and Trafodion were open-sourced within the Apache ecosystem alone. In this talk, Andreas Neumann will introduce and compare the different approaches from various perspectives including scalability, efficiency, operability and portability, and make recommendations pertaining to different use cases. Speaker Bio: Andreas Neumann develops big data software at Cask, and has formerly done so at places that are known for massive scale. He was the chief architect for Hadoop at Yahoo! and also for the foundational content management system that Yahoo! built on Hadoop. Previously he was a research engineer at Yahoo! and a search architect at IBM. Andreas holds a doctoral degree in computer science for his work on querying XML documents.

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Speaker: Sagar Kapare, Cask Big Data Applications Meetup, 05/10/2017 Palo Alto, CA More info here: http://www.meetup.com/BigDataApps/ Link to video: https://youtu.be/mSKwjKvYUtI About the talk: The cost of maintaining a traditional Enterprise Data Warehouse (EDW) is skyrocketing as legacy systems buckle under the weight of exponentially growing data and increasingly complex processing needs. Hadoop, with its massive horizontal scalability, and CDAP which offers pre-built pipelines for EDW Offload in a drag&drop studio environment, can help. Sagar will demonstrate Cask’s solution, which shows how to build code-free, scalable, and enterprise-grade pipelines for delivering an easy-to-use and efficient EDW offload solution. He will also show how interactive data preparation, data pipeline automation, and fast querying capabilities over voluminous data can help unlock new use-cases.

Building Enterprise Grade Applications in Yarn with Apache TwillCask Data

Speaker: Poorna Chandra, from Cask Big Data Applications Meetup, 07/27/2016 Palo Alto, CA More info here: http://www.meetup.com/BigDataApps/ Link to talk: https://www.youtube.com/watch?v=I1GLRXyQlx8 About the talk: Twill is an Apache incubator project that provides higher level abstraction to build distributed systems applications on YARN. Developing distributed applications using YARN is challenging because it does not provide higher level APIs, and lots of boiler plate code needs to be duplicated to deploy applications. Developing YARN applications is typically done by framework developers, like those familiar with Apache Flink or Apache Spark, who need to deploy the framework in a distributed way. By using Twill, application developers need only be familiar with the basics of the Java programming model when using the Twill APIs, so they can focus on solving business problems. In this talk I present how Twill can be leveraged and an example of Cask Data Application Platform (CDAP) that heavily uses Twill for resource management.

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This document discusses using StreamSets Data Collector (SDC) to build a logging infrastructure for microservices. SDC can ingest logs from microservices running in containers and handle issues like schema changes and new log formats. It processes and transforms the logs, sending them to destinations like Kafka. SDC pipelines can run on Spark clusters on Yarn and Mesos to handle large volumes of log data and load it into systems like HDFS, HBase and Elasticsearch for analysis.

NRT Event Processing with Guaranteed Delivery of HTTP Callbacks, HBaseCon 2015Cask Data

HBaseCon 2015 May 7 San Francisco This talk at HBaseCon was given by Poorna Chandra from Cask and Alan Steckley from Salesforce.com Here's a short summary of the talk: Salesforce is building a new service, code-named Webhooks, that enables our customers' own systems to respond in near real-time to system events and customer behavioral actions from the Salesforce Marketing Cloud. The application should process millions of events per day to address the current needs and scale up to billions of events per day for future needs, so horizontal scalability is a primary concern. In this talk, they discussed how Webhooks is built using HBase for data storage and Cask Data Application Platform (CDAP), an open source framework for building applications on Hadoop.

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This document summarizes the architecture of stream processing on files in Reactor. It discusses how streams are written to files for persistence and performance. It describes the directory structure for partitioned files and how writers generate monotonically increasing sequence numbers for files. It also covers tailing streams, filtering events, exactly-once processing, and reconfiguring consumers.

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The document summarizes an agenda for an HBase Meetup at Cask HQ. The agenda includes announcements about Cask's newly open sourced projects - CDAP (Cask Data Application Platform), Coopr (cluster provisioning), and Tigon (real-time streaming on YARN and HBase). It also lists talks on using HBase at Flipboard and master topologies after HBase 1.0. Cask is now fully open source and aims to build communities around these projects to help more developers build applications on Hadoop platforms.

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1. Athena Streaming with Samza Goddess of Wisdom

2. The team

3. Brief Background Stream Processing, Samza, Athena Kafka Uber uses Kafka as logging/event system Message streams Near-real time computation Stream Processing Framework: Apache Samza Platform built on top of Samza: Athena

4. Current use cases

5. Current use cases Aggregation

6. Pricing Assess supply and demand in real time to calculate accurate surge multiples Kafka Samza location updates Elasticsearch S3 Spark location updates HTTP Service Realtime Batch Queries

7. Mapper Job Mapper Job Mapper Job Event parsing, filtering, classification Event Aggregation Raw events Riak Inserter Job Inserter Job Tiles 1m Agg TilesReducer Job Reducer Job 1m Agg Tiles Per Contract Common Artemis Samza

8. Current use cases Event driven update engine

9. Driver Activation KAFKA SAMZA Driver status updates Onboarding Service Fetch driver info RocksDB Retry queue Update status

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11. Fraud monitoring and alerting KAFKA Partitioner Real Time Hourly Alerting Service Monitoring Service Uberx metrics

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21. Athena Manager

24. Job conﬁguration reference.conf Sandbox Staging ProductionDev athena-core-lib application.conf Sandbox Staging ProductionDev Samza job

25. Job conﬁguration projects { artemis { job_1 { mapper { envs.common { task.inputs = "kafka.topic_1" task.class = "com.uber.athena.SampleTaskClass" } envs.local = ${projects.artemis.job_1.mapper.envs.common} envs.local = { task.window.ms = 30000 } envs.sandbox = ${projects.artemis.job_1.mapper.envs.local} envs.sandbox = { yarn.package.path = "http://artifactory..../artifactory/libs-snapshot-local/com/uber/athena/.../hello-athena.tar.gz" } envs.staging = ${projects.artemis.job_1.mapper.envs.sandbox} envs.production = ${projects.artemis.job_1.mapper.envs.sandbox} }

26. Job conﬁguration

27. Unit test framework Stream Job StreamTask InitableTask WindowableTask TaskUnitTestHarness Message Listener Inject data Custom IncomingMessageEnvelope Custom MessageCollector

28. Unit test framework String classTaskName = "com.uber.athena.test.TestProcessTask"; // Register the job to the test harness TaskUnitTestHarness<String, Integer> testProcessTask = new TaskUnitTestHarness<>(classTaskName, false, true); // Register a listener for validating the output of every process function testProcessTask.registerMessageListenerOnProcess(new KeyAsserterListener()); // Start the job testProcessTask.start(); // Inject data testProcessTask.inject(key,value); // Get the full output testWindowTask.getResult()

29. Tooling ●  Athena manager ●  Job configuration ●  Unit test framework ●  Graphite integration ●  Codahale library support ●  Maven archetype ●  Artifactory support

30. Observations

31. Observations ●  YARN is not bad ! ●  Offset lag and Buffered messages ●  Kafka Appender for ELK ●  Checkpoint topic partition incorrect count ●  Config validation needs improvement ●  Job restarts are complicated ●  Built-in Metrics are insufficient

32. ●  Seamless upgrades ●  Custom built-in serde support ●  Config validation enhancement ●  Auto benchmarking a Samza job ●  Unit test framework enhancement Upcoming Samza improvements

33. Q&A

Introducing Athena: 08/19 Big Data Application Meetup, Talk #3

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Introducing Athena: 08/19 Big Data Application Meetup, Talk #3