Large Scale Log Analysis with HBase and Solr at Amadeus (Martin Alig, ETH Zurich)

Large Scale Log Analysis with HBase and
Solr at Amadeus
Martin Alig
aligma@student.ethz.ch

Overview
 Problem
 Solution - Overview
 HBase
 Solr
 Solution - Details
 Results

Montag, 16. Juli 2012 2

Problem

 Amadeus is the worlds leading technology provider
to the travel industry, providing marketing,
distribution and IT services worldwide.
 The Amadeus computer reservation system (CRS)
processed 850 million billable travel transactions in
2010.
 Current logging framework produces 100'000 -
1'000'000 messages per second


Problem - Log Messages

 Messages with 1 KB average size
 Message can be anything: XML, Edifact, HEX
dump, ...
 A few fixed attributes per message given:
Timestamp, source, various ids.


Problem - Current Solution

 Write log messages in plain text files.
 Split, compress and copy to SAN.

Queries? Search? Statistics?


Solution Overview

 Use Apache HBase for storage and instant random
access
 Apache MapReduce for complex queries.
 Apache Solr as full text search engine for queries on
the log messages.


Apache HBase

 Open source, non-relational, distributed database.
 Modeled after Google's BigTable
 Runs on top of Hadoop Distributed Filesystem
(HDFS)


HBase - Terms

 Region
 Contigous ranges of rows stored together
 Dynamically split / merged and distributed
 RegionServer (slave)
 Serves regions, e.g. data for reads and writes
 HMaster (master)
 Responsible for coordination
 Assigns regions to Region Servers, detects failures
 Admin functions


HBase - Architecture

ZooKeeper
HMaster
Client ZooKeeper
HMaster
ZooKeeper

RegionServer RegionServer RegionServer

HDFS


HBase - Data Access

 Java API
 REST
 Apache Avro, Apache Thrift
 Hadoop MapReduce


HBase - Secondary Indexes

 No native support for secondary indexes
 Different choices:
 Client managed: Write value in data table and index in
index table
 Coprocessors that automatically create the secondary index
 Periodic update: Use MapReduce job to add index


HBase - Coprocessors

 Run arbitrary code on any node:
 Observer: RegionObserver, MasterObserver, WALObserver
provide hooks for code execution
(prePut, postPut, preGet, postGet, ...)
 Endpoint: Installed on nodes, executed on client request


Apache Solr

 Apache Lucene + many features like
 Distributed index
 Distributed search
 ...
 Apache Lucene is a high-performance, full-featured
text search engine library


Solution - Details

Client Insert log messages, create
secondary indexes for
predefinded attributes.

HBase

Use coprocessor functionality to index
log messages in Solr after insert.

Solr


Solution - Cluster Configuration
Client Zookeeper Namenode
SecondaryNamenode
HMaster

DataNode DataNode DataNode
RegionServer RegionServer RegionServer
Solr Solr Solr
...

Solution - HBase & MapReduce

 Very good integration of MapReduce into HBase
 Easy to use HBase as data source, data sink or both
 Provides helper classes


Solution - Problems

 Can Solr keep up with HBase?
 Is Solr full text search practical for log messages?
(XML, other formats, ...)


Results

 Not many, yet.
 Generic experiments with random data
 Experiments with real log data just started


Results - Write Random Data - HBase
Only
 Insert random data, 1KB records.
 Cluster configuration:
 5 Nodes:
 RAM: 24 GiB
 CPU: Intel Xeon L5520 2.26
 HD: 2x 15k RPM Sas 73 GB (RAID1)
 1. Node: Master (Namenode, HMaster, Zookeeper)
 2. - 5. Node: Slaves (Datanode, RegionServer)
 Client on seperate node
 Experiment executed with and without secondary
indexes. (5 additional indexes)


Results - Write Random Data - HBase
Only

No secondary indexes Secondary indexs
avg. inserts/sec avg. inserts/sec (not counting
index inserts
~30'000 ~6'000


Results - Write Read Data - HBase & Solr

 No real numbers
 First tests: Single Solr instance indexes ~1000 log
messages per second.


Questions


HBase - Architecure

Source: HBase - The Definitive Guide

HBase - Key Design

Source: HBase - The Definitive Guide

HBase - Hardware

 Master
 Ram: 24 GB
 CPU: Dual quad-core
 Disks: 4 x 1 TB SATA, RAID 0+1
 Slave
 Ram: 24 GB or more
 CPU: Dual quad-core
 Disks: 6 + 1 TB SATA, JBOD


HBase - Monitoring

 Ganglia is a scalable distributed monitoring system
for high-performance computing systems such as
clusters and Grids.
 HBase provides metrics for Ganglia.


Log Message Example (1)

2012/05/15 04:33:04.783757 sitst201 srvT2M-838059 Trace
name: all0302
Message sent [con=19104962 (FE_EXT_TCIL-ISO9735_ETK-
310_OPK2_ETK-REQ), cxn=1498840662
(172.17.39.174:13101), addr=0x1db58830, len=354,
CorrID=000100E1A1EU42,
MsgID=SQ8ZK36LG3TJ12JE6XMU2O8]
UNB^]IATB^_1^]1AETH^_^_LY^]CDBETICKET^_^_LY^]1205
15^_0433^]00JNQPH79K0001^]^]^]O^UNH^]1^]TKCREQ^
_08^_5^_1A^]000100E1A1EU42^DCX^]134^]<DCC
VERS="1.0"><MW><UKEY VAL="EXRU$3013#GJ12V4K#1IZ"
TRXNB="1"/><$



2012/05/15 04:33:04.783671 sitst201 srvT2M-838059 Trace
name: all0302
Query [SAP=1ASICDBETK, DCXID=EXRU$3013#GJ12V4K#1IZ,
TRXNB=1, CorrID=000100E1A1EU42,
MsgID=SQ8ZK36LG3TJ12JE6XMU2O8]



2012/05/15 04:32:42.289282 sitmt301 muxT2-332108 Trace
name: all0302
Message received [con=17697 (inSrvT2_TCIL_1),
cxn=1626671045 (194.156.170.210:8000),
addr=0x13e9b830, len=1710, CorrID=09B5840E,
MsgID=OX7E09RYABBLS61HR2DXTL]
+----- ADDR -----+--------------- HEX ---------------+----- ASCII ----
+---- EBCDIC ----+
0000000013e9b830 554e421d 49415442 1f311d31
4153494c UNB.IATB.1.1ASIL .+.............<
0000000013e9b840 53533243 53544e1d 3141304c
53534352 SS2CSTN.1A0LSSCR ......+....<....
0000000013e9b850 591d3132 30353135 1f303433 321d3030
Y.120515.0432.00 ................ 0000000013e9b860 39 ...

Large Scale Log Analysis with HBase and Solr at Amadeus (Martin Alig, ETH Zurich)

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Large Scale Log Analysis with HBase and Solr at Amadeus (Martin Alig, ETH Zurich)