Spark meetup v2.0.5

HUAWEI TECHNOLOGIES CO., LTD.

Spark SQL on HBASE

Spark Meet-up Tech Talks

Yan Zhou/Bing Xiao

March 25, 2015

HUAWEI TECHNOLOGIES Co., Ltd.

Lead architect of Huawei Big Data Platform

Apache Pig committer

ex-Hadooper @ Yahoo!

15+ years of experience in DB, OLAP, distributed computing fields.

About me

Page ‹2›


A Fortune Global 500, Private Company

Annual growth at 15% (revenue up to $46 Billion in 2014)

More than $6.5 Billion investment on R&D

Transition from telecom-equipment manufacturer to a leader of ICT

(information and communications technology)

Big data and open source are part of company-wide strategies

About Huawei

Page ‹3›


Spark SQL on HBase

o Motivations

o Data Model

o SQL Semantics

o Performance

o Value Proposition

Demo

Roadmap

Q/A

Agenda

Page ‹4›


Driven by use cases in verticals

including telco

Telco Data is unique & complex

Flexible data organization for various

types of queries: range, ad-hoc,

interactive, DW

Stay in tune: planned sessions in

future events

Customer & Billing Data

• Well Structured

• TB

Session Signaling Data：• Multi-Device generated

• Hundreds TB-PB

• Real-Time Biz Oriented

MR/CHR Data：• Semi-structured & Nested

• PB

• Location-centered Data

xDR Data:

• 1~10PB each month

Network Raw Data

• Unstructured

• TB/Sec

• Linear Growth with

Biz

CRM

Billing

Signaling Data

MR/CHR Type Data

xDR Data

Network Raw Data

Note：In a typical network of ~30M subscribers and the data flow around 1TB/Sec.

Page ‹5›

Motivations


NOSQL Key-Value data store on Hadoop

Following Google BigTable model

Emerging platform for scale-out relational data stores on Hadoop:

o Splice Machines

o Trafodion (HP)

o Apache Phoenix (Salesforce)

o Kylin (eBay)

M/R and API-based data access interfaces only

Page ‹6›

What is HBase?


Spark Core

Spark SQL

Existing HBase Access Path

HadoopRDD

TableInput/OutputFormat

Features: Hadoop M/R plug-in

Inflexible/Hard to use

Limited pushdown capabilities

High Latency

HiveContext

metastore

Page ‹7›

HBase


New Data Access Path

metadata

Featuring:

• Fully distributed processing engine for scalability

and fault tolerance

• Scala/Java/Python APIs

• Pluggable data source to Spark SQL through

Spark SQL API

• Enable systematic and powerful handling of

pushdowns (key range, filters, coprocessor)

• More SQL capabilities made possible (Primary Key,

Update, INSERT INTO … VALUES, 2nd index, Bloom Filter, …)

Page ‹8›

Spark SQL

Spark Core

HBase


Logical Data Model the same as Spark SQL: relational and type system.

Physical Data Model:

• Support of composite primary keys

• HBase rowkey of byte representation of composite primary keys

• Logical non-key columns mapped onto <column family, column qualifier>

• Persistent metadata on a special HBase table

• Presplit tables supported

Data Models

Page ‹9›


Based on Spark SQL syntax, plus …

DDL:

• CREATE TABLE table_name (col1 TYPE1, col2 TYPE2, …, PRIMARY KEY (col7, col1, col3))

MAPPED BY (hbase_tablename, COLS=[col2=cf1.cq11, col4=cf1.cq12, col5=cf2.cq21,

col6=cf2.cq22])

•ALTER TABLE table_name ADD/DROP column …

DML:

• INSERT … INTO VALUES …

Bulk Loading:

• LOAD DATA [PARALLEL] INPATH filePath [OVERWRITE] INTO TABLE tableName [FIELDS

TERMINATED BY char]

SQL Semantics

Page ‹10›


Precise partition pruning and partition-specific multidimensional predicate

pushdowns based on partial evaluation of filter boolean expressions for queries

Query Optimization Approach

=> Itemid > 300 AND amount < 30

=> customer=‘John’ AND itemid < 100 AND amount > 200

E.g. a sales table with <customer, itemid> as a 2-dimensional primary key

SELECT * from sales WHERE ((customer=‘Joe’ AND itemid > 300 AND amount < 30) OR

(customer=‘John’ AND itemid < 100) AND amount > 200)

The existing partitions/regions are:

1. (, ‘Ashley’)

2. [‘Ashley’, “Iris”)

3. [(‘Joe’, 10), (‘Joe’, 100)),

4. [(‘Joe’, 200), (‘Joe’, 1000))

5. [‘John’, ‘York’)

6. [‘York’, )

The algorithms are generic and applicable to other organized data sets like

hash-partitioned Hive tables as well.

Suitable for interactive ad hoc queries

Page ‹11›

for scan range for filtering


Queries (TPC-DS, 10M records):

Query Performance

Page ‹12›

SQL Query SparkSQL

on HBase(Seconds)

Phoenix(Seconds)

1-key-range select count(1) from store_sales where

(ss_item_sk = 99 and ss_ticket_number > 1000);

0.18 0.03


(ss_item_sk = 99 and ss_ticket_number > 1000) or

(ss_item_sk = 5000 and ss_ticket_number < 20000);

0.22 4.29


(ss_item_sk = 99 and ss_ticket_number > 1000) or

(ss_item_sk = 5000 and ss_ticket_number < 20000) or

(ss_item_sk = 28000 and ss_ticket_number <= 10000);

0.27 4.44

Aggregate on

the secondary

key

select count(1) from store_sales group by ss_ticket_number; 37 79

• Cluster:

o 1 master + 6 slaves with 48GB/node

o Xeon 2.4G 16 cores


Query performance (TPC-DS, 10M records):

Query Performance

0.18 0.22 0.27

37

0.034.29 4.44

79

0

10

20

30

40

50

60

70

80

90

1-key-range 2-key-range 3-key-range aggregate onsecondary key

Seco

nd

s

Spark SQL on HBase

Phoenix

Page ‹13›


Performance optimization for tabular data bulk loading

• late materialization of KeyValue cells

reduction of shuffle data volume

• removal of sorting by reducers

lightweight reducer

more scalable

• best effort to colocate reducers with the region servers

Optional parallel incremental loading after M/R in the bulk loader

Bulk Load Optimization

Page ‹14›


Loading performance (TPC-DS, 10M records):

Bulk Load Performance

557

185

1093

762

0

200

400

600

800

1000

1200

Load (no presplit) Load (6 presplit regions)

Seco

nd

s

Spark SQL on HBase

Phoenix

Page ‹15›

• Cluster:

o 1 master + 6 slaves with 48GB/node

o Xeon 2.4G 16 cores


Combined capabilities of Spark, SparkSQL and HBase

o Spark Dataframe supported

More traditional DBMS capabilities made possible on HBase

Basis to build a highly performing and concurrent distributed big data SQL

system

Optimized bulk loader for tabular data sets

Performance excellence

Value Proposition

Page ‹16›


Source repo: https://github.com/Huawei-Spark/hbase/

emails: [email protected], [email protected]

Team members:

Bo Meng, Xinyun Huang, Wang Fei, Stanley Burnitt, Shijun(Ken) Ma, Jacky

Li, Stephen Boesch

Plus our Big Data Teams in India and Hangzhou, China

Project Info

Join us …

o Comments, tryouts, and contributions are more than welcome

o Open for Joint development in next phase project(s)

o We are hiring: Big Data Engineers/Spark Fans

Page ‹17›

https://github.com/Huawei-Spark/hbase/


Environment

Hardware: 9-node (1 master + 8 slaves) blades

Software: Linux Enterprise Server 11.1

Data set: TPC-DS 10M records

Indexed-range query vs. full scan query through use of the DataFrame

Join between a Hbase table with an in-memory side table through use of Dataframe

Partial Evaluation

• Schema = (id : Int, age: Int)

• The row to be partially evaluated on is: ([1,5), null)

• Predicate 1: (id < 1) OR (age > 30) => (age > 30)

• Predicate 2: (id < 6) OR (age > 30) => True

Demo

Page ‹18›


Targeting Spark 1.4

Coprocessor/Custom Filter

Filter/Partitions from CAST values in predicates

Latency reduction: Spark-3306 for external resource management

Optimizations of Sorting/Aggregation/Join on primary keys

Support of salting, timestamp, dynamic columns, nested data type, …

Views and materialized views

Future Plan

Page ‹19›


Earliest Corporate sponsor of AMP Lab & its projects including Spark

One of Leading contributors to Spark: 10 & 11 contributors in Spark 1.2 & 1.3

releases

Highlighted contributions of New features : Power Iteration Clustering

represents the first use of GraphX routines within MLLIB; ORCFiles Support;

FP-Growth

Bring Spark & Apps on top of Spark into leading telcos globally as Spark is

cornerstone of Huawei big data vertical solution

Huawei’s Long Term Commitment to Spark & Ecosystem

Page ‹20›


Huawei Planned Spark Roadmap

Spark SQL & Core

1H 2015

Co-Processor, optimization Spark SQL on Hbase

OrcFiles Support

Vectorized Processing

ML & Streaming

2H 2015

Nested Data

Spark on Yarn

Power iteration

Clustering

PAM K-Medoids

Streaming Analysis Algorithms

New requirements

from Spark

implementation

Materialized View

Spark R

GraphX in Telco Data

Modeling

SQL99’2003

Compliance features

Page ‹21›


Reynold Xin, Michael Armbrust at Databricks reviewed Spark SQL

on HBase design document and the code, provided feedback and helped

improve the design

Xiangrui Meng at Databricks provided the guidance, reviewed and

modified the code MLLib Power Iteration Clustering algorithm (Spark 1.3

release) & FP-Growth algorithm (Spark 1.3 release)

Huawei Big Data team in India & Hangzhou, China provided the

performance testing/tuning and participated in code development

Acknowledgements

Page ‹22›

HUAWEI TECHNOLOGIES Co., Ltd.Page ‹23›


Phoenix Architecture and Data Access Path

Phoenix Coprocessor Phoenix Coprocessor Phoenix Coprocessor

Phoenix as

HBase Client

Page ‹24›


HBase vs. Cassandra vs. RDBMS

HBase Cassandra RDBMS

Special Nodes Master Seed Coordinator

Synchronization Mechanism Zookeeper Gossip protocol …

CAP Properties CP AP CA

Data Access Shell, REST,

Java/Thrift API

CQL, Shell, Thrift SQL/JDBC/ODBC

Data Size PBs PBs TBs

Coprocessor/In-DB processing Yes No Yes

Origins Google BigTable Amazon Dynamo

+ Google BigTable

IBM System R

Native to Hadoop

(inclusion in Hadoop distributions,

Hadoop/HDFS specific

optimizations, …)

Yes No No

Index-Organization Single row index Single row index On any columns

Dominant Vendor Backing/Lock-in No Datastax Oracle, MS, IBM

Popular use scenarios Range queries; consistency; fast

reads:

Facebook Messenger

Geographical distributed cluster;

large deployments:

Twitter

Transactions, DW/DM


Spark SQL on HBase

Spark SQL

Spark

Master

HBase Master

Spark Slave Spark Slave

HBase Region

Server

HBase Region

Server

Architecture and Data Access Paths

Zookeeper Quorum

Date post:	18-Jul-2015
Category:	Software
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