Federated Queries with HAWQ - SQL on Hadoop and Beyond

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SPRINGONE2GXWASHINGTON, DC

Federated Queries with HAWQ SQL on Hadoop and Beyond

By Christian Tzolov @christzolov


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Unless otherwise indicated, these s l ides are © 2013-2015 Pivotal Software, Inc. and l icensed under a Creat ive Commons Attr ibut ion-NonCommercial l icense: ht tp: / /creat ivecommons.org/ l icenses/by-nc/3.0/ 3

BioChristian Tzolov

Pivotal Technical Architect,

BigData, Hadoop, SpringXD

Apache Committer, Crunch PMC member

[email protected]

@christzolov

blog.tzolov.net

http://bit.ly/1NuLut0

mailto:[email protected]?subject=

http://blog.tzolov.net

http://blog.pivotal.io/big-data-pivotal/pivotal-people/christian-tzolov-on-open-source-engineering-the-odp-and-pivotal


Contents

• Data System Concepts• Data System Reference Architectures.• Real Life Use Case: OLTP and OLAP Data Systems Integration• Passive Data Synchronization (Demo)• Federated Queries With HAWQ• HAWQ Web Tables• HAWQ PXF Architecture• Geode PXF (Demo)

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How Compute Arbitrary Functions

on Arbitrary Data

Query = Function (All Data)

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Data System Concepts

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Data

What is Data?

• Data is Information

• Derived vs. Fact Information

• Not derived information will be called Data

• Data hold to be true simply because it exists

• All other information is derived from the Data

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Data System

System that computes arbitrary functions on arbitrary data.

• Low Latency

• High Throughput

• Scalability

• Ad hoc queries

• Provenance

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Data System Principles

The 6-elements Application = Data-Native System

• Fact Data

• Immutable Data

• Deterministic Functions

• Data-Lineage

• Data Locality - space or temporal

• All Data vs. Working Set

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Data System – Key Properties

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Data System Architectures

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Data System Architectural Patterns

How to build Data Systems?

• Data Lake

• Lambda

• Kappa / Samza

• Tachyon

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Data Lake (From ETL to ELT)

Single Store For All Data

• Store Raw Data

• No Data Schema Limitations

• No Data Volume Limitations

• Late evaluation

• Extract-Transform-Load (ETL) vs. Extract-Load-Transform (ELT)

• Data Lineage

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• Batch Layer

• Serving Layer

• Speed Layer

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Lambda Architecture


• Durable Queues (Kafka)• Replaying History• Versioned Outputs• Dynamic Output Switch

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Kappa Architecture


Tachyon

No write speed constrains

• Data-Lineage Based Fault Tolerance

• Asynchronous Checkpoints

• Unbounded Writes

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Real Life Use Case

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Real Life Use Case

• Integrate an In-Memory Data Grid - Geode (e.g. GemFire) with SQL-On-Hadoop OLAP system - HAWQ

• Provide an unified data view across both systems

• Use Geode as Slowly Changing Dimensions (SCDs) store for HAWQ

• Keep the Operational and Historical data in Sync

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• Cache - Performance / Consistency / Resiliency

• Region

• Distributed j.u.Map on steroids

• Highly available, redundant

• Distributed

• Locator, Server, Client

Apache Geode & GemFire (OLTP)

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Geode use cases…

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China RailwayCorporation

5,700 train stations4.5 million tickets per day20 million daily users1.4 billion page views per day40,000 visits per second

* http://pivotal.io/big-data/pivotal-gemfire

Indian Railways

7,000 stations72,000 miles of track23 million passengers daily120,000 concurrent users10,000 transactions per minute

http://pivotal.io/big-data/pivotal-gemfire


HAWQ (SQL-on-HADOOP)

• Enterprise SQL on Hadoop analytic engine built around a high-performance Greenplum MPP Database

• HAWQ runs natively on Apache Hadoop, HDFS and YARN.• Supports multiple Hadoop file formats: Parquet, HDFS and Avro• 100% ANSI SQL compliant (ANSI SQL-92, SQL-99, and SQL-2003…)• ODBC and JDBC connectivity• Implemented in C and C++

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HAWQ (SQL-on-HADOOP)

• Runs TPC-DS benchmark in half the wall clock time that Impala • Beats Impala by overall 454% in performance• Compared to Hive, HAWQ provides an additional of 344% of performance

improvement on complex queries• Impala and Apache Hive™ do not support all standard TPC-DS queries• HAWQ runs 100% of them natively.

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Test Environment

• HDFS – Distributed file system. PHD or HDP Hadoop distributions.

• HAWQ - SQL-on-Hadoop.

• Geode/GemFire - In-memory data grid.

• SpringXD – runtime integration and streaming platform.

• Apache Ambari – Manages all Clusters.

• Apache Zeppelin – Web interface for interacting with different Data Systems.

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GEODE Locator, Servers

HAWQ Master,

Segments

HDFS NameNode, DataNodes

SPRING XD

Admin, Containers

Zeppelin Notebook

Ambari


Test Environment - Ambari Management

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Passive Data Synchronization

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Passive Synchronization (Case 1)

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Passive Synchronization (Case 1) - Demo

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https://www.youtube.com/watch?v=C1EHTkZu1BY


Passive Synchronization (Case 2) - gpfdist

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Passive Synchronization (Case 2): Demo

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http://youtu.be/uSUBe8Hgk8w


Federated Queries With HAWQ

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Access dynamic data sources on a web server or by executing OS scripts.

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HAWQ Web Table – Architecture


HAWQ Web Table

• HAWQ Web Table - access dynamic data sources on a web server or by executing OS scripts.

• Leverage Geode REST API and OQL.

• RESTful SpringBoot app to convert JSON into TSV.

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CREATE EXTERNAL WEB TABLE EMPLOYEE_WEB_TABLE (...) EXECUTE E'curl http://<adapter proxy>/gemfire-api/v1/ queries/adhoc?q=<URLencoded OQL statement>' ON MASTER FORMAT 'text' (delimiter '|' null 'null' escape E'\\');

smb://'


HAWQ Web Table – Limitations

• Not Scalable

• No “Filter Push Down”

• Inflexibility

• No Compression

• Requires Additional Components

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HAWQ PXF/Geode External Table

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Pivotal eXtension Framework (PXF)

• Java-Based

• Parallel, High Throughput Data Access

• Heterogeneous Data Sources.

• ANSI-compliant SQL On Any Dataset

• Wide variety of PXF plugins

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PXF Architecture

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PXF Data Model

• Data Source is modeled as a collection of one or more Fragments.

• Each Fragment consists of many Rows that in turn are split into typed Fields.

• Analyzer (optional) provides PXF statistical data for the HAWQ query optimizer

• Metadata about the data source locations, access attributes, table schemas formats, SQL queries filters, etc

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PXF Processors

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PXF Runtime Model

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CREATE EXTERNAL TABLE ext_table_name <attr list, ...> LOCATION('pxf://<host>:<port>/path/to/data? FRAGMENTER=package.name.FragmenterForX& ACCESSOR=package.name.AccessorForX& RESOLVER=package.name.ResolverForX& <Other custom user options>=<value>') FORMAT ‘custom'(formatter='pxfwritable_import');

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PXF External Tables Definition


Available PXF Plugins

• HdfsTextSimple• HdfsTextMulti• Hive• HiveRC• HiveText• Hbase• Avro

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• Accumulo• Casandra• JSON• Redis• Geode/Gemfire• Pipes


Build Geode PXF Plugin

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<profile> <name>GEMFIRE</name> <description>A profile for reading Gemfire data</description> <plugins> <fragmenter>io.pivotal.pxf.plugins.gemfire.GemfireFragmenter</fragmenter> <accessor>io.pivotal.pxf.plugins.gemfire.GemfireAccessor</accessor> <resolver>io.pivotal.pxf.plugins.gemfire.GemfireResolver</resolver> </plugins> </profile>

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PXF Geode Profile


Geode External Table Definition

CREATE EXTERNAL TABLE <GEMFIRE_TABLE_NAME> (...) LOCATION('pxf://<namenode>/<path>? PROFILE=GEMFIRE & LOCATORS=<gemfire-server:port> & REGION=<region-name>') FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

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public class GemfireFragmenter extends Fragmenter { public GemfireFragmenter(InputData inputData) throws Exception { super(inputData); }

public List<Fragment> getFragments() throws Exception { return Arrays.asList (new Fragment( inputData.getDataSource(), new String[] { inputData.getUserProperty("URL-HOST") }, new byte[0])); }}

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Geode Fragmenter


public class GemfireAccessor extends Plugin implements ReadAccessor { public GemfireAccessor(InputData input) throws Exception { super(input); }

public boolean openForRead() throws Exception { … } public OneRow readNextObject() throws Exception {…}

public void closeForRead() throws Exception {…}}

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Geode Accessor


public class GemfireResolver extends Plugin implements ReadResolver {

public GemfireResolver(InputData input) { super(input); } public List<OneField> getFields(OneRow paramOneRow) throws Exception { }}

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Geode Resolver


Demo 3: Federated queries with PXF/Geode

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Demo 3: Federated queries with PXF/Geode

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By Christian Tzolov @christzolov

HADOOP WORKFLOWS AND DISTRIBUTED YARN APPS USING SPRING TECHNOLOGIES

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Federated Queries with HAWQ - SQL on Hadoop and Beyond

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