Partners 2013 LinkedIn Use Cases for Teradata Connectors for Hadoop

LINKEDIN USE CASES FOR
TERADATA CONNECTORS
FOR HADOOP
Eric Sun:
Jason Chen:

esun@LinkedIn.com
www.linkedin.com/in/ericsun
jason.chen@Teradata.com
www.linkedin.com/in/jason8chen

Agenda
• UDA
> Need for high volume data movement
> Challenges with high volume data movement

• TDCH & SQL-H
> Data movement between Teradata and Hadoop
> Architecture, key features, and various packages

• LinkedIn POC
> Architecture big picture
> Use cases
> POC environment
> POC results and learning
> Wish list of enhancements

• Next steps and Q&A

Copyright © Teradata 2013

TERADATA UNIFIED DATA ARCHITECTURE
Data Scientists

Quants
Business Analysts

Engineers

LANGUAGES

VIEWPOINT

Customers / Partners
Executives

MATH & STATS

DATA MINING

Aster Connector for
Hadoop

Operational Systems

BUSINESS INTELLIGENCE

Aster Teradata
Connector

DISCOVERY
PLATFORM

Front-Line Workers

SQL-H

Teradata Connector
for Hadoop

SQL-H

Teradata Studio Smart
Loader for Hadoop

CAPTURE | STORE | REFINE

IMAGES

TEXT

WEB & SOCIAL

MACHINE LOGS


SUPPORT

INTEGRATED
DATA WAREHOUSE

Data Fabric

AUDIO & VIDEO

APPLICATIONS

CRM

SCM

ERP

Data Movement Challenges
• Data Movement supposed to be Easy
- So businesses can spend more time on analytics
- But it is not easy as businesses would like in reality
- Challenges are even greater with massively parallel systems

• Data Movement between Teradata and Hadoop
> Two massively parallel systems
> Any high volume data movement

– Should exploit as much underlying parallelism as appropriate
– Single–threaded or single-node processing architecture will not cut it
– Move data along the path in compressed form for as long as possible
> Various popular Hadoop data formats

– Should be supported to avoid the need for staging & intermediate files
– Automatic data type/format conversion to minimize manual work by users
> Constraints in a production environment

– Should be accommodated as much as possible
– E.g., limitation on concurrent sessions imposed by mixed workload control


Can technologies work with each other?


Big Data Army Together


TDCH Architecture

TD
Export
Import
Tools

Hive

Sqoop

HCat

Pig

MapReduce

Hadoop

I/O Format
DB I/O Format

Teradata

Teradata
I/O Format

…

File I/O Format
Text

Sequence

RC

Hadoop DFS
Teradata DB

TDCH Technical Highlights
• Build on MapReduce
- For execution and deployment scalability
- Proven scalability for up to thousands of nodes

- For integration with various data formats
- Text, Sequence, RC, ORC (soon), Avro (soon), …

- For integration with other MR based tools
- Sqoop, Hive, Hcatalog, Pig (future), and possibly others

• Built for Usability & Integration
> Simple command-line interface
> Simple application programming interface for developers
> Metadata-based data extraction, load, and conversion
– Built-in support for reading/writing data in Hive and Hcat tables
– Built-in serialization/de-serialization support for various file formats


TDCH Export Methods (Hadoop  Teradata)

Various Implementations
• Batch-insert
- Each mapper starts a session to insert data via JDBC batch
execution
• Multiple-fastload
- Each mapper starts a separate fastload job to load data via
JDBC fastload
• Internal-fastload
- Each mapper starts a session to load data via JDBC fastload
protocol but all sessions are operating as a single fastload job


TDCH Import Methods (Teradata  Hadoop)
Various Implementations
• Split-by-value
- Each mapper starts a session to retrieve data in a given value range from

a source table in Teradata

• Split-by-hash
- Each mapper starts a session to retrieve data in a given hash value range

from a source table in Teradata

• Split-by-partition
- Each mapper starts a session to retrieve a subset of partitions from a

source table in Teradata if the source table is already a partitioned table

- If the source table is not a partitioned table, a partitioned staging table

will be created with a partition key that is the same as the distribution key

• Split-by-amp
- Each mapper gets data from an individual amp
- TD 14.10 required; this method makes use of table operators


Various Packages for End Users
> Teradata Connector for Hadoop
– For users who would like to use a simple command line interface

> Sqoop Connectors for Teradata
– For users who would like to use the Sqoop command line interface
– Sqoop connector for TD from Hortonworks uses TDCH under the cover
– New Sqoop connector for TD from Cloudera uses TDCH under the cover

> TD Studio Smart Loader for Hadoop
– For users who would like to use the Teradata Studio GUI
– TD Studio Smart Loader for Hadoop uses TDCH under the cover for data
movement between Terada and Hadoop


Teradata SQL-H
• SQL-H
> Build on table operators
> Enable dynamic SQL access to Hadoop data
> Can list existing Hadoop database and files
> SQL requests parsed and executed by Teradata
> Can join data in Hadoop with tables in Teradata

• Why is this important?
> Enables analysis of Hadoop data in Teradata
> Allow standard ANSI SQL access to Hadoop data
> Lowers costs by making data analysts self-sufficient

> Leverage existing BI tool investments just like Aster SQL-H does

• Released with Teradata Database 14.10


Teradata SQL-H Example
SELECT CAST(Price AS DECIMAL (8,2))
,CAST(Make AS VARCHAR(20))
,CAST(Model AS VARCHAR(20))
FROM LOAD_FROM_HCATALOG(
USING
SERVER('sdll4364.labs.teradata.com')
PORT('9083')
USERNAME ('hive')
DBNAME('default')
TABLENAME('CarPriceData')
COLUMNS('*')
TEMPLETON_PORT('1880')
) as CarPriceInfo;


The SQL-H Table Operator
query is launched on the
Teradata side.



Data conversion is conducted within
Teradata after the data has been
transferred from Hadoop.


Connectors Designed for Two Different Audiences

Teradata

Hadoop

ETL Tools
BI Tools
HCat

Pig

Sqoop

Teradata
Tools

SQL-H

Hive

TD Connector for Hadoop (TDCH)
Teradata
SQL

MapReduce

Text

Sequence

HDFS

Teradata DB
TDCH: Scalable, high performance bi-directional data movement


RC

The Challenge with Hadoop
• Hadoop
> Excellent scalability
> Rapidly evolving ecosystem
> Not yet as enterprise-ready as one would like
– Lacking support for effective performance management

• Challenge (and opportunity)
> Enterprise tools and apps to fill the gap
> Provide the instrumentation and functionality
– For fine-grain (parallel systems) performance management

• TDCH is improving
> with richer instrumentation and functionality
> to fill the performance management gap as much as possible


LinkedIn Overall Data Flow
Hadoop
Site
(Member
Facing
Products)

Activity
Data

Kafka

Camus

Member Data

Espresso /
Voldemort /
Oracle

DWH ETL

Product,
Sciences,
Enterprise
Analytics

Changes

Databus

External
Partner Data

Lumos

Ingest
Utilities

Teradata

Enterprise
Products

Core Data
Set

Derived
Data Set

Computed Results for Member Facing Products

LinkedIn Data System - Hadoop

Most data in Avro format

Access via Pig & Hive

Most High-volume
ETL processes run here

Specialized batch processing
Algorithmic data mining


LinkedIn Data System - Teradata

Interactive Querying
(Low Latency)

Integrated Data Warehouse
Hourly ETL
Well-modeled Schemas

Workload Management

Standard BI Tools


LinkedIn Use Cases
• Hadoop is the main platform for data staging, data
exploration, click stream ETL, and machine learning;
• Teradata is the main platform for data warehouse, BI and
relational data discovery;
• Hadoop holds multi-PB data; TD holds hundred-TB data;
• Data need to flow between Hadoop and Teradata;
• Analytical processes and applications need to leverage the
most appropriate platform to deliver the data intelligence:
> Are all the data needed there? (1-week/3-month/3-year…)
> Which programming interfaces are available? (SQL/HQL/Pig…)
> How fast I need/How slow I can tolerate?
> How to share the results? Who will consume them?


LinkedIn TDCH POC Environment


LinkedIn Use Cases - Export
• Copy Hourly/Daily Clickstream Data from HDFS to TD
• Copy Scoring & Machine Learning Result from HDFS to TD
> Challenges: Big volume and tight SLA
> Steps:
1. Converted data files from Avro to many ^Z-delimited *.gz files
via Pig first
(flatten map/bag/tuple, and remove special unicode chars)
2. Quickly load *.gz files using Teradata Connector into the staging
table with the help of internal.fastload protocol
3. TDCH execute INSERT DML to copy records from the staging table
into the final fact table
>

Other Options:
1. Combine many *.gz into a few, download to NFS, load via TPT
2. Download many *.gz via webHDFS to NFS, load via TPT


LinkedIn Use Cases - Export


LinkedIn Use Cases - Import
• Publish dimension and aggregate tables from TD to HDFS
> Challenges: Heavy query workload on TD and tight SLA.

Traditional JDBC data dump does not yield high throughput to
extract all the dimensional tables within the limited window.
> Steps:
1. Full dump for small to medium size dimensional tables
2. Timestamp-based incremental for big dimensional tables
Then use M/R job to merge the incremental file with the existing
dimensional file on HDFS
Save the new dimensional file using LiAvroStorage() as #LATEST
copy, and retire the previous version
3. Date-partition-based incremental dump for aggregate tables

>

Other Options:
1. Home-grown M/R job to extract using split.by.partition and write
to LiAvroStorage() directory
2. Write Custom TPT OUTMOD Adapter to convert EXPORT operator’s
data parcel to Avro, upload via webHDFS


Partners 2013 LinkedIn Use Cases for Teradata Connectors for Hadoop

High Level Findings

split.by.value is not tested

Network latency plays a
big factor to E2E speed

# sessions is subject
to TD workload rules


LinkedIn POC Benchmark Reference
Test Data Set:
> About 275M rows and 250 bytes/row
> 2700MB in TD with BLC and 2044MB as GZip text in HDFS
> 64664MB as uncompressed text

• Import uses split-by-hash & Export uses internal-fastload
* Import will spend the first a couple of minutes to spool data
* Export M/R job may combine the specified # of mappers to smaller #

# Mappers

32

64

128

960s

758s

330s

67MB/sec

85MB/sec

190MB/sec

# Mappers
Export

Import Time

Throughput

Import

15

28

52

Import Time

970s

870s

420s

Throughput

67MB/sec

75MB/sec

154MB/sec


LinkedIn POC Findings
• TDCH is very easy to setup and use
• TDCH provides good throughput for JDBC-based bulk data
movement (import and export)
• TDCH simplifies the bulk data exchange interface, so more
robust ETL system can rely on TDCH
• Network latency can be a big performance factor
• In production environment, it is not practical to execute
TDCH with too many mappers (e.g. over 150+) for TDCH
• Depends on the data set, using too many mappers will not
result in performance gain (because the overhead is high)
• Many factors can impact E2E performance, debug is hard
> Some mappers can run for much longer than the others even

with the similar number of records to process
> Multiple mappers can run on the same DD – is that wrong?

LinkedIn Business Benefits/Results
• LinkedIn can have simpler methods to move and access
data seamlessly throughout their environment using the
Teradata Connector for Hadoop.
• This leads to reduced costs and operation complexity
because
> Command is invoked from Hadoop gateway machine, so the

security verification is taken care by SSH session and
Kerberos token already.
> The data synchronization between HDFS and Teradata is
faster with the help of both systems’ parallel capability.
> Less ETL jobs are needed for the move data movement,
hence easier to support and troubleshoot.


Sample Code - Export
hadoop com.teradata.hadoop.tool.TeradataExportTool
-D mapred.job.queue.name=marathon
-D mapred.job.name=tdch.export_from_rcfile_to_td.table_name.no_blc
-D mapred.map.max.attempts=1
-D mapred.child.java.opts="-Xmx1G -Djava.security.egd=file:/dev/./urandom"
-D mapreduce.job.max.split.locations=256
-libjars $LIB_JARS
-url jdbc:teradata://DWDEV/database=DWH,CHARSET=UTF8
-username DataCopy -password $DEV_DATACOPY_PASS
-classname com.teradata.jdbc.TeraDriver
-queryband "App=TDCH;ClientHost=$HOSTNAME;PID=$$;BLOCKCOMPRESSION=NO;"
-fileformat rcfile -jobtype hive -method internal.fastload
-sourcepaths
/user/$USER/tdch/example_table_name.rc
-debughdfsfile /user/$USER/tdch/mapper_debug_info
-nummappers 32
-targettable dwh.tdch_example_table_name
-sourcetableschema "COL_PK BIGINT, SORT_ID SMALLINT, COL_FLAG TINYINT,
ORDER_ID INT, ORDER_STATE STRING, ..., ORDER_UPDATE_TIME TIMESTAMP"

Sample Code - Import
hadoop com.teradata.hadoop.tool.TeradataImportTool
-D mapred.job.queue.name=marathon
-D mapred.job.name=tdch.import_from_td_to_textfile.table_name
-D mapred.map.max.attempts=1
-D mapred.child.java.opts="-Xmx1G -Djava.security.egd=file:/dev/./urandom"
-D mapred.output.compress=true
-D mapred.output.compression.codec=org.apache.hadoop.io.compress.LzoCodec
-libjars $LIB_JARS
-url jdbc:teradata://DWDEV/database=DWH,CHARSET=UTF8
-username DataCopy -password $DEV_DATACOPY_PASS
-classname com.teradata.jdbc.TeraDriver
-queryband "App=TDCH;ClientHost=$HOSTNAME;PID=$$;"
-fileformat textfile -jobtype hdfs –method split.by.hash
-targetpaths
/user/$USER/tdch/example_table_name.text
-debughdfsfile /user/$USER/tdch/mapper_debug_info
-nummappers 32
-sourcetable dwh.tdch_example_table_name

Mapper Level Performance Info
-debughdfsfile option (sample output)
mapper id is: task_201307092106_634296_m_000000
initialization time of this mapper is: 1382143274810
elapsed time of connection created of this mapper is: 992
total elapsed time of query execution and first record returned
is:221472
total elapsed time of data processing and HDFS write operation
is:296364
end time of this mapper is: 1382143848579

mapper id is: task_201307092106_634273_m_000025
initialization time of this mapper is: 1382143015468
elapsed time of connection created of this mapper is: 463
total elapsed time of data processing and send it to Teradata
is:701876
end time of this mapper is: 1382143720637


Track Mapper Session in DBQL
TDCH injects task attempt id into QueryBand
Select SubStr( RegExp_SubStr(QueryBand,
'=attempt_[[:digit:]]+_[[:digit:]]+_.*[[:digit:]]+'), 10 ) MR_Task,
SessionID, QueryID,
min(StartTime), min(FirstStepTime), min(FirstRespTime),
sum(NumResultRows), cast(sum(SpoolUsage) as bigint) SpoolUsage,
sum(TotalIOCount), max(MaxIOAmpNumber)
from DBC.DBQLogTbl
where StatementGroup not like 'Other'
and NumResultRows > 0
and UserName = 'DataCopy'
and CollectTimeStamp >= timestamp '2013-10-17 09:10:11'
and QueryBand like '%attemp_id=attempt_201310161616_118033_%'
group by 1,2,3
order by 1, SessionID, QueryID;
* This feature does not work for internal-fastload yet

Adjust TCP Send/Receive Window
• 1ms ~ 70ms network round trip time (traceroute) can be
the indicator of suboptimal latency, which will significantly
affect TDCH throughput
• If the network just has bad latency without dropping many
packets, increase the TCP window buffer from its default
value 64K to 6MB~8MB can improve TDCH performance
• The result varies based the network and data set’s size
> When data set size for each mapper is small, no visible

improvement is observed
> When data set size for each mapper is big, on a network with
high latency, the TDCH throughput can improve 30% ~ 100%
with big TCP window
> TCP window size has impact on both Import and Export jobs
(Import) jdbc:teradata://tdpid/database=DBC,TCP=RECEIVE8192000
(Export) jdbc:teradata://tdpid/database=DBC,TCP=SEND8192000

Wish List based on LinkedIn POC
• Compression over the wire/network protocol
> If JDBC and FASTLOAD session can compress records and then

transmit, the TDCH speed can be 3~10 times faster.
> Otherwise, a pair of data import/export proxy agents can help to
buffer, consolidate and compress the network traffic

• Split-By-Partition enhancement
> TDCH can create many partitions for stage table to avoid data

skew (e.g. # partitions = # AMPs)
> But it can then effectively loop these partitions through 32 or 64
sessions without further consuming too much spool

• Avro format support with simple capability of mapping
element/attribute from map/record/array… to columns
• Auto map TD data types to RC & Avro primitive types
• Easier way to use special chars as parameter in command
• More meaningful error message for mapper failure
• Better and granular performance trace and debug info

What is Next?
• Turn learning into TDCH enhancements
> Data formats:
– Support Avro and Optimized RC

> Metadata access:
– Use dictionary tables through views without extra privileges
> Performance management:
– Instrument for fine-grain monitoring and efficient trouble shooting

• Start proof-of-concept work with SQL-H
> Was in the original plan but ran out of time
> Will start the SQL-H POC after release upgrade to TD 14.10


Acknowledgement
• Many have contributed to this effort …
• LinkedIn:
> Eric Sun, Jerry Wang, Mark Wagner, Mohammad Islam

• Teradata:
> Bob Hahn, Zoom Han, Ariff Kassam, David Kunz, Ming Lei,

Paul Lett, Mark Li, Deron Ma, Hau Nguyen, Xu Sun, Darrick
Sogabe, Rick Stellwagen, Todd Sylvester, Sherry Wang,
Wenny Wang, Nick Xie, Dehui Zhang, …


Email: esun@LinkedIn.com
Email: jason.chen@Teradata.com
PARTNERS Mobile App
InfoHub Kiosks
teradata-partners.com


Partners 2013 LinkedIn Use Cases for Teradata Connectors for Hadoop

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