snappydata overview slidedeck for big data bellevue

SnappyData Getting Spark ready for real-time,

operational analytics

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Suds Menon Co-Founder SnappyData

Jan 2016

Last Week Tonight in Big Data

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IoT is what makes the big data challenge very real

A 10 Trillion Device World1

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1:http://cacm.acm.org/news/191847-get-ready-to-live-in-a-trillion-device-world/fulltext

Because Insights are like people. Useful for a short period of time

The New Arms Race

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●  Sift through data to get insights to improve your business

●  What is your time to insights? ●  What is your time to

operationalizing insights?

Can we use the past to accurately predict the future?

The Holy Grail of Analytics

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The faster you go, the bigger your business advantage

Speeding Up Insights

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Exploding data volumes fuel the search for distributed solutions

How We Got Here

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Teradata Cognos

GreenPlum Netezza, ParAccel

Hadoop (SQL on Hadoop)

Spark (Spark SQL)

Every enterprise today deals with these 4 kinds of data interactions

The Four Horsemen Of Data

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OLTP OLAP Streaming Machine Learning

Who Are We? ●  An EMC-Pivotal spinout focused on real time operational

analytics ●  New Spark-based open source project started by Pivotal

GemFire founders+engineers

●  Decades of in-memory data management experience

●  Focus on real-time, operational analytics: Spark inside an OLTP+OLAP database

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SnappyData At Cruising Altitude

Single unified HA cluster: OLTP + OLAP + Stream for real-time analytics

Batch design, high throughput

Real time operational Analytics – TBs in memory

RDB

Rows Txn

Columnar

API

Stream processing

ODBC, JDBC, REST

Spark - Scala, Java, Python, R

HDFS AQP

First commercial project on Approximate Query Processing(AQP)

MPP DB

Index

SnappyData: A new approach

Single unified HA cluster: OLTP + OLAP + Stream for real-time analytics

Batch design, high throughput

Real-­‐time  design  center  -­‐  Low  latency,  HA,  

concurrent  

Vision: Drastically reduce the cost and complexity in modern big data

Huge community adoption, slip streaming into Hadoop momentum, great data integration platform

Why Spark? •  Most events in life can be analyzed as micro batches •  Blends streaming, interactive, and batch analytics •  Appeals to Java, R, Python, Scala programmers •  Rich set of transformations and libraries •  RDD and fault tolerance without replication •  Offers Spark SQL as a key capability

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Spark is a compute framework that processes data, not an analytics database

Clearing Up Some Spark Myths

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●  It is NOT a distributed in-memory database ○  It’s a computational framework with immutable caching

●  It is NOT Highly Available ○  Fault tolerance is not the same as HA

●  NOT well suited for real time, operational environments ○  Does not handle concurrency well ○  Does not share data very well either

SnappyData & Lambda

SnappyData Focus

Perspective on Lambda for real time

In-Memory DB

Interactive queries, updates

Deep Scale, High volume

MPP DB Transform Data-in-motion Analytics

Application

Streams

Alerts

RELEVANT USECASES

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Market Surveillance

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FLAG DETECT

ANALYZE INGEST

Identify patterns based on query results

Partitioned, HA stream ingestion

Prevent settlement, investigate further

SQL queries & Stream Analytics on microbatches

Contextual Marketing

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RESPOND DECIDE

ANALYZE INGEST

Pick Ad based on variety of reference data parameters

Transactional request for Ad placement

Deliver in real time

Join with history, join with user profile, join with location

Location Based Telco Services

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Geo Fencing Mobile Marketing Network Analytics

●  INGEST, CORRELATE, JOIN WITH HISTORICAL DATA, RESPOND

Spark Architecture

Driver

Cluster Manager (YARN, Mesos,

Standalone)

Worker Worker

Worker

Executor

REST API for Job

Submission

Worker Worker

Worker Data Server

Executor

Cluster Manager (YARN, Mesos,

Standalone)

Data Server

Executor

Snappy Infused Spark Architecture

JDBC Clients

ODBC Clients

Job Server Lead Node Lead Node

Core Components Of SnappyData

Colocated row/column Tables in Spark

Row Table

Column Table

Spark Executor TASK

Spark Block Manager

Stream processing

Row Table

Column Table

Spark Executor TASK

Spark Block Manager

Stream processing

Row Table

Column Table

Spark Executor TASK

Spark Block Manager

Stream processing

●  Spark Executors are long lived and shared across multiple apps ●  Gem Memory Mgr and Spark Block Mgr integrated

Table can be partitioned or replicated

Replicated Table

Partitioned Table (Buckets A-H) Replicated

Table

Partitioned Table (Buckets I-P)

consistent replica on each node

Partition Replica (Buckets A-H)

Replicated Table

Partitioned Table (Buckets Q-W) Partition

Replica (Buckets I-P)

Data partitioned with one or more replicas

Linearly scale with shared partitions

Spark Executor

Spark Executor

Kafka queue

Subscriber N-Z

Subscriber A-M

Subscriber A-M Ref data

Linearly scale with partition pruning Input queue, Stream, IMDB, Output queue all share the same partitioning strategy

Point access, updates, fast writes

●  Row tables with PKs are distributed HashMaps ○  with secondary indexes

●  Support for transactional semantics ○  read_committed, repeatable_read

●  Support for scalable high write rates ○  streaming data goes through stages ○  queue streams, intermediate storage (Delta row buffer),

immutable compressed columns

Full Spark Compatibility ●  Any table is also visible as a DataFrame

●  Any RDD[T]/DataFrame can be stored in SnappyData tables

●  Tables appear like any JDBC sourced table ○  But, in executor memory by default

●  Addtional API for updates, inserts, deletes //Save a dataFrame using the spark context …

context.createExternalTable(”T1", "ROW", myDataFrame.schema, props ); //save using DataFrame API dataDF.write.format("ROW").mode(SaveMode.Append).options(props).saveAsTable(”T1");

Extends Spark CREATE  [Temporary]  TABLE  [IF  NOT  EXISTS]  table_name        (                <column  deIinition>          )    USING  ‘JDBC  |  ROW  |  COLUMN  ’  OPTIONS  (        COLOCATE_WITH  'table_name',        //  Default  none        PARTITION_BY  'PRIMARY  KEY  |  column  name',  //  will  be  a  replicated  table,  by  default        REDUNDANCY                '1'  ,          //  Manage  HA      PERSISTENT      "DISKSTORE_NAME  ASYNCHRONOUS  |    SYNCHRONOUS",    

     //  Empty  string  will  map  to  default  disk  store.        OFFHEAP  "true  |  false"        EVICTION_BY    "MEMSIZE  200  |  COUNT  200  |  HEAPPERCENT",  …..      [AS  select_statement];  

Key feature: Synopses Data ●  Maintain stratified samples

○  Intelligent sampling to keep error bounds low

●  Probabilistic data ○  TopK for time series (using time aggregation CMS, item

aggregation) ○  Histograms, HyperLogLog, Bloom Filters, Wavelets

CREATE SAMPLE TABLE sample-table-name USING columnar OPTIONS (

BASETABLE ‘table_name’ // source column table or stream table [ SAMPLINGMETHOD "stratified | uniform" ] STRATA name ( QCS (“comma-separated-column-names”) [ FRACTION “frac” ] ),+ // one or more QCS

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AQP Architecture

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Spot The Differences

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SnappyData is Open Source ●  Beta will be on github in January. We are looking for

contributors!

●  Learn more & register for beta: www.snappydata.io

●  Connect: ○  twitter: www.twitter.com/snappydata ○  facebook: www.facebook.com/snappydata ○  linkedin: www.linkedin.com/snappydata ○  slack: http://snappydata-slackin.herokuapp.com ○  IRC: irc.freenode.net #snappydata

Q&A

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THANK YOU

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