achieving 100k queries per hour on hive on tez

May 1, 2023

Achieving 100k Queries per Hour with Hive on Tez

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About Yahoo! JAPAN

The Largest Portal Site in Japan

65 billon pageviews / month

2.1 billon pageviews / day

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YDN Report

What is YDN Report?• Report for Yahoo Display Ads. Networks

Batch Reporting over Massive Dataset• 13 months, 800B+ rows of data• Adding 3.3B+ rows of data per day

Highly Parallel Workload• 100K reports per hour

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YDN Report Query

Typical Query• Query is Relatively Simple• Answer “How many clicks did I get last week?”

0

5000

10000

15000

SELECT account, yyyymmdd, sum(total_imps), sum(total_click),

... FROM table_x WHERE yyyymmdd >= xxx

AND yyyymmdd < xxx AND account = xxx ... GROUP BY account, yyyymmdd, ...;

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Test Setup

Hive Performance Recap

Hive is fast: interactive response• ORC columnar file format• Cost based optimizer (CBO)• Vectorized SQL engine• Tez execution engine (replacing MapReduce)

Hive 0.10Batch Processing 100-150x Query Speedup

Hive 1.2HumanInteractive (5 seconds)

Hive on Tez Query Execution

A query execution essentially is put together from• Client execution [ 0s if done correctly ]• Optimization [HiveServer2] [~ 0.1s]• Metadata lookups [Hcatalog, Metastore] [ very fast in hive 0.14 ]• Application Master creation [4-5s]• Container Allocation [3-5s]• Tez task execution on YARN

YARN and HDFS

HiveServer2Server #1Client

Running testing tool

N connections

N connections

Metastore Metastore DB

HiveServer2Server #2

TezAM

TezContain

er

TezContaine

r…

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Mini Test

Mini Setup Tested• 50 nodes• 450B rows dataset• Achieved 15K queries per hour

So, can we get 100K qph on 700 nodes?

We thought it should be easy, but…

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The Bottlenecks at Scale

Challenges at Scale• Hive Metastore Server• YARN Resource Manager• Datanode Hotspot• YARN ATS

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Hive Metastore Server

Use Local Metastore• Before: HS2 -> Metastore Server -> Metastore DB• After: HS2 (local metastore) -> Metastore DB

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Hive Metastore Server

Use Local Metastore• Throughput: 7.6K -> 22K qph

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Pending Apps

YARN ResourceManager Scalability• Too much pending apps

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Pending Apps

YARN ResourceManager Scalability• Too much pending apps• Resolve: increase

yarn.resourcemanager.amlauncher.thread-count• Throughput: 22K -> 26K qph

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Pending Containers

YARN ResourceManager Scalability• Too much pending containers

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Pending Containers

YARN ResourceManager Scalability• Too much pending containers• Resolve: increase tez.am-rm.heartbeat.interval-

ms.max • Throughput: 26K -> 72.5K qph

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Datanode Hotspot

Last Hour Problem• Connection timeout and disk access error• Many queries access recently added data

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Datanode Hotspot

Last Hour Problem• Resolve: Increase HDFS replication factor• Throughput: 72.5K -> 95K qph

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Other Tunings

Other Tunings We Did• Container reuse timeout• YARN capacity scheduler node locality delay• Tez shuffle keep alive• TCP fin_wait

Notes on YARN ATS• Disabling YARN ATS gives higher throughput• Trade off losing YARN log aggregation

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End of first half

End of first half

Yohei Abe@Yahoo! JAPAN

Real-life Hive LLAP at Yahoo! JAPAN

Aug 2016

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Agenda

• Hive LLAP at Yahoo! JAPAN

• Tuning• Performance Result• Future Work

Hive LLAP at Yahoo! JAPAN

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Hive on Tez

Hive on Tez is able to produce 100K reports/hour

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Hive on Tez+LLAP

How Hive on Tez+LLAP handle 100K reports ?

• how many servers • Tuning?

What is LLAP

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What is LLAP?

LLAP is for sub-second query procesisng

•Persistent daemons

•Caching data

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What is LLAP?

Tez container

Tez container

Tez AppMaster

Tez

created dynamically

LLAPdaemon

LLAPdaemon

Tez AppMaster

Tez+LLAP

persistent daemon

Basic Tuning

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LLAP test cluster

Server node Xeon E5-2660v2 2.2GHz / 2CPU / 128GBMEM / 10GBase-T 2port

Slave node 45 nodesHiveServer2 node 10 nodesHadoop 2.7.1Hive 2.1.0-snapshotTez 0.8.3

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Parameters

Some basic parameters needs to be changed

very slow performance if it’s default value

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Threading model

hive.llap.daemon.num.executors

hive.llap.io.threadpool.size

threadexecutor

thread

threadI/O

thread

data

Executor thread pool

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hive.llap.daemon.num.executors (default 4)• the number of JVM thread for

query execution• set this same with the num of

vCPU• 40 in our cpu

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Performance: executor thread

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I/O thread pool

hive.llap.io.threadpool.size(default 10)• number of IO threads• Set the number of vCPU

• 40 in our case

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Performance: I/O thread

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Memoryhive.llap.daemon.memory.per.instance.mb java -Xmx …

hive.llap.io.memory.size

executor I/O

JVM on-heap JVM off-heap

Performance(compared to Tez)

Performance: QPS

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100K / hour ?

LLAP 45 nodes(test cluster)

max: 24 qps ≈ 87K query/hour

70 nodes for 100K(if it’s scaled linearly)

Advanced Tuning

Advanced Tuning

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hive.llap.client.consistent.splits

false(default) => Use file locality for selecting LLAP daemon

true => LLAP daemon is selected evenly(by hash distribution)

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Recap: LLAP

A node runs LLAPand also datanode

hive.llap.client.consistent.splits

43 Locality No Locality

Future Work

Web UI

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Web UI (HIVE-11526)LLAP daemon exposes basic metrics on port 15002(default)

Included in HIVE2.1

Contributed from Yahoo! JAPAN

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Web UI (HIVE-14030)

HIVE-11526 is just for each daemon

HIVE-14030 provides aggregation view of a LLAP cluster (not yet in master)

Contributed from Yahoo! JAPAN

ACL

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Hive Column-level ACL

HS2 LLAP

YARN

HDFS

GOAL: Column-level ACL

SQL

ANSWER(?):HiveServer2 can do it

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Direct Access to HDFSbreaks everything

HS2 LLAP

YARN

HDFS

Storage Based Authorization

M/R,Pig,

SparkBreak SQL

Standard Based ACLs

!!

But direct accessing(Not from Hive) to HDFS breaks the security model.

Other solutions(not only Hive)are necessary

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Future Directions

HS2 LLAP

YARN

HDFSLlapInputFormat

M/R,Pig,

Spark

CheckSQL

Based ACLs

LlapInputFormat checks ACLs to HS2 for other applications.HIVE-13441 HIVE-12991

see LlapDump.java

Summary

Summary

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• Throughput is greatly improved by LLAP

• Some tunings are necessary

• LLAP is also effective for batch processing

Q & A