A Study of I/O and Virtualization Performance with a Search Engine

based on an XML database and Lucene

Ed Bueché, EMC edward.bueche@emc.com, May 25, 2011

Agenda

§  My Background §  Documentum xPlore Context and History §  Overview of Documentum xPlore §  Tips and Observations on IO and Host

Virtualization

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My Background §  Ed Bueché §  Information Intelligence Group within EMC §  EMC Distinguished Engineer & xPlore Architect §  Areas of expertise

•  Content Management (especially performance & scalability)

•  Database (SQL and XML) and Full text search •  Previous experience: Sybase and Bell Labs

§  Part of the EMC Documentum xPlore development team •  Pleasanton (CA), Grenoble (France), Shanghai,

and Rotterdam (Netherlands) 4

Documentum search 101 •  Documentum Content Server provides an “object/

relational” data model and query language —  Object metadata called “attributes” (sample: title, subject,

author) —  Sub-types can be created with customer defined attributes —  Documentum Query Language (DQL) —  Example:

SELECT object_name FROM foo WHERE subject = ‘bar’ AND customer_id = ‘ID1234’

•  DQL also support full text extensions —  Example:

SELECT object_name FROM foo SEARCH DOCUMENT CONTAINS ‘hello world’ WHERE subject = ‘bar’ AND customer_id = ‘ID1234’

Introducing Documentum xPlore

§  Provides ‘Integrated Search’ for Documentum •  but is built as a

standalone search engine to replace FAST Instream

§  Built over EMC xDB, Lucene, and leading content extraction and linguistic analysis software

Documentum Search History-at-a-glance §  almost 15 years of Structured/Unstructured integrated search

Verity Integration 1996 – 2005 • Basic full text search through DQL • Basic attribute search • 1 day à 1 hour latency • Embedded implementation

FAST Integration 2005 – 2011 • Combined structured / unstructured search • 2 – 5 min latency • Score ordered results

xPlore Integration 2010 - ??? •  Replaces FAST in DCTM •  Integrated security •  Deep facet computation •  HA/DR improvements •  Latency: typically seconds

Improved Administration •  Virtualization Support

1996 2010 2005

Enhancing Documentum Deployments with Search

•  Without Full Text in a Documentum deployment a DQL query will be directed to the RDBMS –  DQL is translated into SQL

•  However, relational querying has many limitations….

Content Server DCTM client

DQL SQL

RDBMS

search

Enhancing Documentum Deployments with Search

• DQL for search can be directed to the full text engine instead of RDBMS (FTDQL) • This allows query to be serviced by xPlore • In this case DQL is translated into xQuery (the query language of xPlore / xDB)

Content Server

Documentum client

DQL SQL

xQuery

RDBMS

Metadata + content

search

Some Basic Design Concepts behind Documentum xPlore

§  Inverted Indexes are not optimized for all use-cases •  B+-tree indexes can be far more efficient for

simple, low-latency/highly dynamic scenarios §  De-normalization can’t efficiently solve all

problems •  Update propagation problem can be deadly •  Joins are a necessary part of most applications

§  Applications need fine control over not only search criteria, but also result sets

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Design concepts (con’t) §  Applications need fluid, changing metadata

schemas that can be efficiently queried •  Adding metadata through joins with side-tables

can be inefficient to query §  Users want the power of Information Retrieval

on their structured queries §  Data Management, HA, DR shouldn’t be an

after-thought §  When possible, operate within standards §  Lucene is not a database. Most Lucene

applications deploy with databases.

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Lessons Learned…

Structured Query use-cases

Unstructured Query use-cases

Fit to use-case

Indexes, DB, and IR

Structured Query use-cases

Unstructured Query use-cases

Relational DB technology

Fit to use-case

Scoring, Relevance,

Entities

Hierarchical data representations

(XML)

Full Text searches

Constantly changing schemas

Indexes, DB, and IR

Structured Query use-cases

Unstructured Query use-cases

Fit to use-case

Full Text index technology

Meta data query

Transactions

Advanced data management (partitions)

JOINs

Indexes, DB, and IR

Structured Query use-cases

Unstructured Query use-cases

Relational DB technology

Fit to use-case

Full Text index technology

Documentum xPlore

•  Bring  best-­‐of-­‐breed  XML  Database  with  powerful  Apache  Lucene  Fulltext  Engine  

•  Provides  structured  and  unstructured  search  leveraging  XML  and  XQuery  standards  

•  Designed  with  Enterprise  readiness,  scalability  and  ingesCon  

•  Advanced  Data  Management  funcConality  necessary  for  large  scale  systems  

•  Industry  leading  linguisCc  technology  and  comprehensive  format  filters  

•  Metrics  and  AnalyCcs  

xDB Transaction, Index & Page Management

xDB Query Processing& Optimization

xDB API

xPlore API Search

Services

Node & Data Management

Services

Indexing Services

Admin Services

Content Processing Services

Analytics

EMC xDB: Native XML database §  Formerly XHive database

•  100% java •  XML stored in “persistent DOM” format

§  Each XML node can be located through a 64 bit identifier §  Structure mapped to pages §  Easy to operate on GB XML files

•  Full Transactional Database •  Query Language: XQuery with full text extensions

§  Indexing & Optimization •  Palette of index options optimizer can pick from •  At it simplest: indexLookup(key) à node id

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Scope of index covers all xml files in all sub-libraries

A

B C

Libraries / Collections & Indexes

A

B

C

= xDB segment

= xDB Library / xPlore collection = xDB Index = xDB xml file ( dftxml , tracking xml, status, metrics, audit)

Lucene Integration §  Transactional

•  Non-committed index updates in separate (typically in memory) lucene indexes

•  Recently committed (but dirty) indexes backed by xDB log

•  Query to “index” leverages Lucene multi-searcher with filter to apply update/delete blacklisting

§  Lucene indexes managed to fit into xDB’s ARIES-based recovery mechanism

§  No changes to Lucene •  Goal: no obstacles to be as current as possible

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Lucene Integration (con’t) §  Both value and full text queries supported

•  XML elements mapped to lucene fields •  Tokenized and value-based fields available

§  Composite key queries supported •  Lucene much more flexible than traditional B-

tree composite indexes §  ACL and Facet information stored in Lucene

field array •  Documentum’s security ACL security model

highly complex and potentially dynamic •  Enables “secure facet” computation

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xPlore has lucene search engine capabilities plus….

ü  XQuery provides powerful query & data manipulation language •  A typical search engine can’t even express a join •  Creation of arbitrary structure for result set •  Ability to call to language-based functions or java-

based methods ü  Ability to use B-tree based indexes when needed

•  xDB optimizer decides this ü  Transactional update and recovery of data/index ü  Hierarchical data modeling capability

Tips and Observations on IO and Host Virtualization

§  Virtualization offers huge savings for companies through consolidation and automation

§  Both Disk and Host virtualization available §  However, there are pitfalls to avoid

•  One-size-fits-all •  Consolidation contention •  Availability of resources

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Tip #1: Don’t assume that one-size-fits all

§  Most IT shops will create “VM or SAN templates” that have a fixed resource consumption •  Reduces admin costs •  Example: Two CPU VM with 2 GB of memory •  Deviations from this must be made in a special

request §  Recommendations:

•  Size correctly, don’t accept insufficient resources •  Test pre-production environments

Same concept applies for disk virtualization §  The capacity of disks are

typically expressed in terms of two metrics: space and I/O capacity •  Space defined in terms of

GBytes •  I/O capacity defined in terms

of I/O’s per sec §  NAS and SAN are forms of disk

virtualization •  The space associated with a

SAN volume (for example) could be striped over multiple disks

•  The more disks allocated, the higher the I/O capacity

50GB and 100 I/O’s per sec capacity

50GB and 200 I/O’s per sec capacity

50GB and 400 I/O’s per sec capacity

Linear mapping’s and Luns

§  When mapped directly to physical disks then this could concentrate I/O to fewer than a desired set of drives.

§  High-end SAN’s

like Symmetrix can handle this situation with virtual LUN’s

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Allocated  for  Index

Logical  volume  with  linear  mapping

Four  Luns

Free  space  in  volume

EMC Symmetrix: Nondisruptive Mobility Virtual LUN VP Mobility

§ Fast, efficient mobility

§ Maintains replication and quality of service during relocations

§ Supports up to thousands of concurrent VP LUN migrations

§ Recommendation: work with storage technicians to ensure backend storage has sufficient I/O

Virtual Pools

Flash 400 GB RAID 5

Tier 2

Fibre Channel 600 GB 15K RAID 1

SATA 2 TB RAID 6

VLUN

Tip #2: Consolidation Contention §  Virtualization provides benefit from consolidation §  Consolidation provides resources to the ‘active’

•  Your resources can be consumed by other VM’s, other apps

•  Physical resources can be over-stretched §  Recommendations:

•  Track actual capacity vs. planned §  Vmware: track number of times your VM is denied CPU §  SANs: track % I/O utilization vs. number of I/O’s

•  For Vmware leverage guaranteed minimum resource allocations and/or allocate to non-overloaded HW

Some Vmware statistics §  Ready metric

•  Generated by Vcenter and represents the number of cycles (across all CPUs) in which VM was denied CPU

•  Generated in milliseconds and “real-time” sample happens at best every 20 secs

•  For interactive apps: As a percentage of offered capacity > 10% is considered worrisome

§  Pages-in, Pages-out •  Can indicate over subscription of memory

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Sample %Ready for a production VM with xPlore deployment for an entire week

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0%2%4%6%8%

10%12%14%16%

“official” area that Indicates pain

In this case Avg resp time doubled and max resp time grew by 5x

Actual Ready samples during several hour period

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0

500

1000

1500

2000

2500

Ready  samples  (#  of  millisecs  VM  denied  CPU  in  20  sec  intervals)

Some Subtleties with Interactive CPU denial

§  The Ready metric represents denial upon demand •  Interactive workloads can be bursty •  If no demand, then Ready counter will be low

§  Poor user response encourages less usage •  Like walking on a broken leg •  Causing less Ready samples

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20 sec interval

Denial spike

Sharing I/O capacity

§  If Multiple VM’s (or servers) are sharing the same underlying physical volumes and the capacity is not managed properly •  then the available I/O capacity of the volume could

be less than the theoretical capacity §  This can be seen if the OS tools show that the

disk is very busy (high utilization) while the number of I/Os is lower than expected

Volume for Lucene application

Volume for other application

Both volumes spread over the same set of drives and effectively sharing the I/O capacity

Recommendations on diagnosing disk I/O related issues §  On Linux/UNIX

•  Have IT group install SAR and IOSTAT §  Also install a disk I/O testing tool (like ‘Bonnie’)

•  Compare ‘Bonnie’ output with SAR & IOSTAT data §  High disk Utilization at much lower achieved rates could

indicate contention from other applications •  Also, High SAR I/O wait time might be an

indication of slow disks §  On Windows

•  Leverage the Windows Performance Monitor •  Objects: Processor, Physical Disk, Memory

Sample output from the Bonnie tool

¹ Bonnie is an open source disk I/O driver tool for Linux that can be useful for pretesting Linux disk environments prior to an xPlore/Lucene install.

bonnie -s 1024 -y -u -o_direct -v 10 -p 10 This will increase the size of the file to 2 Gb. Examine the output. Focus on the random I/O area: ---Sequential Output (sync)----- ---Sequential Input-- --Rnd Seek- -CharUnlk- -DIOBlock- -DRewrite- -CharUnlk- -DIOBlock- --04k (10)- Machine MB K/sec %CPU K/sec %CPU K/sec %CPU K/sec %CPU K/sec %CPU /sec %CPU Mach2 10*2024 73928 97 104142 5.3 26246 2.9 8872 22.5 43794 1.9 735.7 15.2

-s 1024 means that 2 GB files will be created

-o_direct means that direct I/O (by-passing buffer cache) will be done

-v 10 means that 10 different 2GB files will be created.

-p 10 means that 10 different threads will query those files

This output means that the random read test saw 735 random I/O’s per sec at 15% CPU busy

Linux indicators compared to bonnie output

Device: tps kB_read/s kB_wrtn/s kB_read kB_wrtn sde 206.10 2402.40 0.80 24024 8

09:29:17 DEV tps rd_sec/s wr_sec/s avgrq-sz avgqu-sz await svctm %util 09:29:27 dev8-65 209.24 4877.97 1.62 23.32 1.62 7.75 3.80 79.59

09:29:17 PM CPU %user %nice %system %iowait %steal %idle

09:29:27 PM all 41.37 0.00 5.56 29.86 0.00 23.21

09:29:27 PM 0 62.44 0.00 10.56 25.38 0.00 1.62

09:29:27 PM 1 30.90 0.00 4.26 35.56 0.00 29.28

09:29:27 PM 2 36.35 0.00 3.96 30.76 0.00 28.93

09:29:27 PM 3 35.77 0.00 3.46 27.64 0.00 33.13

I/O stat output:

SAR –d output:

SAR –u output:

Notice that at 200+ I/Os per sec the underlying volume is 80% busy. Although there could be multiple causes, one could be that some other VM is consuming the remaining I/O capacity (735 – 209 = 500+).

High I/O wait See https://community.emc.com/docs/DOC-9179 for additional example

Tip #3: Try to ensure availability of resources §  Similar to the previous issue,

but •  resource displacement not

caused by overload, •  Inactivity can cause Lucene

resources to be displaced •  Not different from running on

large shared native OS host §  Recommendation:

•  Periodic warmup §  non-intrusive

•  See next example

IO / caching test use-case §  Unselective Term search

•  100 sample queries •  Avg( hits per term) = 4,300+, max ~ 60,000 •  Searching over 100’s of DCTM object attributes + content

§  Medium result window •  Avg( results returned per query) = 350 (max: 800)

§  Stored Fields Utilized •  Some security & facet info

§  Goal: •  Pre-cache portions of the index to improve response time in

scenarios •  Reboot, buffer cache contention, & vm memory contention

Some xPlore Structures for Search¹

Dictionary of terms Posting list (doc-id’s for term)

Stored fields (facets and node-ids)

Security indexes (b-tree based)

xDB XML store (contains text for summary)

1st doc N-th doc

Facet decompression map

¹Frequency and position structures ignored for simplicity

IO model for search in xPlore Search Term: ‘term1 term2’

Dictionary Posting list (doc-id’s for term)

Stored fields

Xdb node-id plus facet / security info

Security lookup (b-tree based)

xDB XML store (contains text for summary)

Result set

Facet decompression map

Separation of “covering values” in stored fields and summary

Facet Calc

FinalFacet calc values over thousands of results Res-1 - sum Res-2 - sum Res-3 - sum : : Res-350-sum

Xdb docs with text for summary

Small number for result window

Small structure

Potentially thousands of results

Stored fields (Random access)

Potentially thousands of hits

Security lookup

xPlore Memory Pool areas at-a-glance

xPlore Instance (fixed size)

memory

xDB Buffer Cache

Lucene Caches

& working memory

xPlore caches

Other vm working

mem

Operating System

File Buffer cache

(dynamically sized)

Native code content extraction & linguistic processing memory

Lucene data resides primarily in OS buffer cache

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xPlore Instance (fixed size)

memory

xDB Buffer Cache

LuceneCaches

& working memory

xPlorecaches

Other vmworking

mem

Operating System

File Buffer cache

(dynamically sized)

Native code content extraction & linguistic processing memory

Dictionary of termsPosting list (doc-id’s for term)

Stored fields (facets and node-ids)

1st doc N-th doc

xDB XML store (contains text for summary)

N-th doc

Potential for many things to sweep lucene from that cache

Test Env §  32 GB memory §  Direct attached storage (no SAN) §  1.4 million documents §  Lucene index size = 10 GB §  Size of internal parts of Lucene CFS file

•  Stored fields (fdt, fdx): 230 MB (2% of index) •  Term Dictionary (tis,tii): 537 MB (5% of index) •  Positions (prx): 8.78 GB (80% of index) •  Frequencies (frq) : 1.4 GB (13 % of index)

§  Text in xDB stored compressed separately

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Some results of the query suite Test Avg Resp

to consume all results (sec)

MB pre-cached

I/O per result

Total MB loaded into memory (cached + test)

Nothing cached 1.89 0 0.89 77 Stored fields cached 0.95 241 0.38 272 Term dict cached 1.73 537 0.79 604

Positions cached 1.58 8,789 0.74 8,800

Frequencies cached 1.65 1,406 0.63 1,436

Entire index cached 0.59 10,970 < 0.05 10,970

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•  Linux buffer cache cleared completely before each run •  Resp as seen by final user in Documentum •  Facets not computed in this example. Just a result set returned. With Facets

response time difference more pronounced. •  Mileage will vary depending on a series of factors that include query complexity,

compositions of the index, and number of results consumed

Other Notes §  Caching 2% of index yields a response time

that is only 60% greater than if the entire index was cached. •  Caching cost only 9 secs on a mirrored drive pair •  Caching cost 6800 large sequential I/O’s vs.

potentially 58,000 random I/O’s §  Mileage will vary, factors include

•  Phrase search •  Wildcard search •  Multi-term search

§  SAN’s can grow I/O capacity as search complexity increases

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Contact §  Ed Bueché

•  edward.bueche@emc.com •  http://community.emc.com/people/Ed_Bueche/blog •  http://community.emc.com/docs/DOC-8945

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