Hadoop and Hive Development at Facebook

Hadoop and Hive Development at
Facebook

Dhruba Borthakur Zheng Shao
{dhruba, zshao}@facebook.com
Presented at Hadoop World, New York
October 2, 2009

Who generates this data?

  Lots of data is generated on Facebook
–  300+ million active users
–  30 million users update their statuses at least once each
day
–  More than 1 billion photos uploaded each month
–  More than 10 million videos uploaded each month
–  More than 1 billion pieces of content (web links, news
stories, blog posts, notes, photos, etc.) shared each
week

Data Usage

  Statistics per day:
–  4 TB of compressed new data added per day
–  135TB of compressed data scanned per day
–  7500+ Hive jobs on production cluster per day
–  80K compute hours per day
  Barrier to entry is significantly reduced:
–  New engineers go though a Hive training session
–  ~200 people/month run jobs on Hadoop/Hive
–  Analysts (non-engineers) use Hadoop through Hive

Where is this data stored?

  Hadoop/Hive Warehouse
–  4800 cores, 5.5 PetaBytes
–  12 TB per node
–  Two level network topology
  1 Gbit/sec from node to rack switch
  4 Gbit/sec to top level rack switch

Data Flow into Hadoop Cloud
Network 
Storage 
and 
Servers 
Web Servers 
Scribe MidTier 

Oracle RAC  Hadoop Hive Warehouse  MySQL

Hadoop Scribe: Avoid Costly Filers
Scribe Writers 

RealBme 
Hadoop 
Cluster 
Web Servers  Scribe MidTier 

Oracle RAC  Hadoop Hive Warehouse  MySQL 
http://hadoopblog.blogspot.com/2009/06/hdfs-scribe-integration.html

HDFS Raid

  Start the same: triplicate
every data block
A B C
  Background encoding
–  Combine third replica of A B C
blocks from a single file to
create parity block A B C
–  Remove third replica
–  Apache JIRA HDFS-503 A+B+C
  DiskReduce from CMU
–  Garth Gibson research A file with three blocks A, B and C

http://hadoopblog.blogspot.com/2009/08/hdfs-and-erasure-codes-hdfs-raid.html

Archival: Move old data to cheap storage

Hadoop Warehouse 

NFS 

Hadoop Archive Node  Cheap NAS 

Hadoop Archival Cluster 

hEp://issues.apache.org/jira/browse/HDFS‐220 
Hive Query

Dynamic-size MapReduce Clusters

  Why multiple compute clouds in Facebook?
–  Users unaware of resources needed by job
–  Absence of flexible Job Isolation techniques
–  Provide adequate SLAs for jobs
  Dynamically move nodes between clusters
–  Based on load and configured policies
–  Apache Jira MAPREDUCE-1044

Resource Aware Scheduling (Fair Share
Scheduler)
  We use the Hadoop Fair Share Scheduler
–  Scheduler unaware of memory needed by job
  Memory and CPU aware scheduling
–  RealTime gathering of CPU and memory usage
–  Scheduler analyzes memory consumption in realtime
–  Scheduler fair-shares memory usage among jobs
–  Slot-less scheduling of tasks (in future)
–  Apache Jira MAPREDUCE-961

Hive – Data Warehouse

  Efficient SQL to Map-Reduce Compiler

  Mar 2008: Started at Facebook
  May 2009: Release 0.3.0 available
  Now: Preparing for release 0.4.0

  Countable for 95%+ of Hadoop jobs @ Facebook
  Used by ~200 engineers and business analysts at Facebook
every month

Hive Architecture

Web UI + Hive CLI + JDBC/ Map Reduce HDFS
ODBC User-defined
Browse, Query, DDL Map-reduce Scripts

MetaStore Hive QL UDF/UDAF
substr
Parser sum
Thrift API average
Planner Execution
FileFormats
SerDe
Optimizer TextFile
CSV SequenceFile
Thrift RCFile
Regex

Hive DDL

  DDL
–  Complex columns
–  Partitions
–  Buckets
  Example
–  CREATE TABLE sales (
id INT,
items ARRAY<STRUCT<id:INT, name:STRING>>,
extra MAP<STRING, STRING>
) PARTITIONED BY (ds STRING)
CLUSTERED BY (id) INTO 32 BUCKETS;

Hive Query Language

  SQL
–  Where
–  Group By
–  Equi-Join
–  Sub query in from clause
  Example
–  SELECT r.*, s.*
FROM r JOIN (
SELECT key, count(1) as count
FROM s
GROUP BY key) s
ON r.key = s.key
WHERE s.count > 100;

Group By

  4 different plans based on:
–  Does data have skew?
–  partial aggregation
  Map-side hash aggregation
–  In-memory hash table in mapper to do partial
aggregations
  2-map-reduce aggregation
–  For distinct queries with skew and large cardinality

Join

  Normal map-reduce Join
–  Mapper sends all rows with the same key to a single
reducer
–  Reducer does the join
  Map-side Join
–  Mapper loads the whole small table and a portion of big
table
–  Mapper does the join
–  Much faster than map-reduce join

Sampling

  Efficient sampling
–  Table can be bucketed
–  Each bucket is a file
–  Sampling can choose some buckets
  Example
–  SELECT product_id, sum(price)
FROM sales TABLESAMPLE (BUCKET 1 OUT OF 32)
GROUP BY product_id

Multi-table Group-By/Insert

FROM users
INSERT INTO TABLE pv_gender_sum
SELECT gender, count(DISTINCT userid)
GROUP BY gender
INSERT INTO
DIRECTORY '/user/facebook/tmp/pv_age_sum.dir'
SELECT age, count(DISTINCT userid)
GROUP BY age
INSERT INTO LOCAL DIRECTORY '/home/me/pv_age_sum.dir'
SELECT country, gender, count(DISTINCT userid)
GROUP BY country, gender;

File Formats

  TextFile:
–  Easy for other applications to write/read
–  Gzip text files are not splittable
  SequenceFile:
–  Only hadoop can read it
–  Support splittable compression
  RCFile: Block-based columnar storage
–  Use SequenceFile block format
–  Columnar storage inside a block
–  25% smaller compressed size
–  On-par or better query performance depending on the query

SerDe

  Serialization/Deserialization
  Row Format
–  CSV (LazySimpleSerDe)
–  Thrift (ThriftSerDe)
–  Regex (RegexSerDe)
–  Hive Binary Format (LazyBinarySerDe)
  LazySimpleSerDe and LazyBinarySerDe
–  Deserialize the field when needed
–  Reuse objects across different rows
–  Text and Binary format

UDF/UDAF

  Features:
–  Use either Java or Hadoop Objects (int, Integer, IntWritable)
–  Overloading
–  Variable-length arguments
–  Partial aggregation for UDAF
  Example UDF:
–  public class UDFExampleAdd extends UDF {
public int evaluate(int a, int b) {
return a + b;
}
}

Hive – Performance

Date SVN Revision Major Changes Query A Query B Query C
2/22/2009 746906 Before Lazy Deserialization 83 sec 98 sec 183 sec
2/23/2009 747293 Lazy Deserialization 40 sec 66 sec 185 sec
3/6/2009 751166 Map-side Aggregation 22 sec 67 sec 182 sec
4/29/2009 770074 Object Reuse 21 sec 49 sec 130 sec
6/3/2009 781633 Map-side Join * 21 sec 48 sec 132 sec
8/5/2009 801497 Lazy Binary Format * 21 sec 48 sec 132 sec

  QueryA: SELECT count(1) FROM t;
  QueryB: SELECT concat(concat(concat(a,b),c),d) FROM t;
  QueryC: SELECT * FROM t;
  map-side time only (incl. GzipCodec for comp/decompression)
  * These two features need to be tested with other queries.

Hive – Future Works

  Indexes
  Create table as select
  Views / variables
  Explode operator
  In/Exists sub queries
  Leverage sort/bucket information in Join

Hadoop and Hive Development at Facebook

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