A Study of I/O and Virtualization Performance with a Search Engine based on an XML database and Lucene Ed BuechĂŠ, 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 xPlore Integration 2010 - ??? Verity Integration 1996 – 2005 • Basic full text search through DQL • Basic attribute search • 1 day à 1 hour latency • Embedded implementation
1996
FAST Integration 2005 – 2011 • Combined structured / unstructured search • 2 – 5 min latency • Score ordered results
2005
• • • • • •
Replaces FAST in DCTM Integrated security Deep facet computation HA/DR improvements Latency: typically seconds Improved Administration Virtualization Support
2010
Enhancing Documentum Deployments with Search RDBMS search
DCTM client
DQL
Content Server
SQL
• 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
.
Enhancing Documentum Deployments with Search RDBMS
search
Documentum client
DQL
Content Server
• 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)
SQL
xQuery
Metadata + content
Some Basic Design Concepts behind Documentum xPlore § Inverted Indexes are not optimized for all usecases • 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 10
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. 11
Lessons Learned‌
Fit to use-case
Structured Query use-cases
Unstructured Query use-cases
Indexes, DB, and IR Full Text searches Hierarchical data representations (XML)
Fit to use-case
Relational DB technology
Structured Query use-cases
Constantly changing schemas Scoring, Relevance, Entities
Unstructured Query use-cases
Indexes, DB, and IR Meta data query
JOINs
Fit to use-case
Advanced data management (partitions) Transactions
Structured Query use-cases
Full Text index technology Unstructured Query use-cases
Indexes, DB, and IR
Fit to use-case
Relational DB technology
Structured Query use-cases
Full Text index technology Unstructured Query use-cases
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
xPlore API Indexing Services
Search Services
Content Processing Services
Node & Data Management Services
Analytics
Admin Services
xDB API xDB Query Processing& Optimization xDB Transaction, Index & Page Management
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 17
Libraries / Collections & Indexes
= xDB Library / xPlore collection = xDB Index
A
B
= xDB xml file (dftxml, tracking xml, status, metrics, audit)
C
= xDB segment
Scope of index covers all xml files in all sub-libraries A
C B
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 19
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 Btree 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 20
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 javabased 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 Four Luns
Logical volume with linear mapping Allocated for Index
Free space in volume
§ 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 25
EMC Symmetrix: Nondisruptive Mobility Virtual LUN VP Mobility § Fast, efficient mobility
Virtual Pools Flash 400 GB RAID 5
Fibre Channel 600 GB 15K RAID 1
SATA 2 TB RAID 6
Tier 2
V L U N
§ 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
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 nonoverloaded 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 16% 14%
12% 10%
In this case Avg resp time doubled and max resp time grew by 5x
official area that Indicates pain
8% 6%
4% 2% 0%
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Actual Ready samples during several hour period Ready samples (# of millisecs VM denied CPU in 20 sec intervals) 2500 2000 1500 1000 500 0
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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
Denial spike
20 sec interval 31
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 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 ยน 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.
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 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+).
I/O stat output: Device: sde
tps 206.10
kB_read/s 2402.40
kB_wrtn/s 0.80
kB_read 24024
kB_wrtn 8
SAR –d output: 09:29:17 09:29:27
DEV dev8-65
tps 209.24
rd_sec/s 4877.97
wr_sec/s 1.62
avgrq-sz 23.32
avgqu-sz 1.62
await 7.75
svctm 3.80
%util 79.59
SAR –u output: 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
See https://community.emc.com/docs/DOC-9179 for additional example
High I/O wait
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 Searchยน Dictionary of terms Posting list (doc-id s for term)
Stored fields (facets and node-ids) 1st doc
Facet decompression map
N-th doc
Security indexes (b-tree based)
ยนFrequency and position structures ignored for simplicity
xDB XML store (contains text for summary)
IO model for search in xPlore Search Term: term1 term2 Dictionary
Result set Posting list (doc-id s for term)
Stored fields Xdb node-id plus facet / security info
Facet decompression map
Security lookup (b-tree based)
xDB XML store (contains text for summary)
Separation of covering values in stored fields and summary Potentially thousands of hits
Potentially thousands of results
Small structure
Security lookup
Stored fields (Random access)
Facet Calc
Small number for result window Xdb docs with text for summary
FinalFacet calc values over thousands of results Res-1 - sum Res-2 - sum Res-3 - sum : : Res-350-sum
xPlore Memory Pool areas at-a-glance Other vm working mem
xPlore caches
Lucene Caches & working memory
xDB Buffer Cache
Native code content extraction & linguistic processing memory
Operating System File Buffer cache (dynamically sized)
xPlore Instance (fixed size) memory
Lucene data resides primarily in OS buffer cache Dictionary of terms Posting list (doc-id’s for term)
N-th doc
xDB XML store (contains text for summary)
xPlore caches
Other vm working mem
Lucene Caches & working memory
Stored fields (facets and node-ids) 1st doc
xDB Buffer Cache
Native code content extraction & linguistic processing memory
Operating System File Buffer cache
(dynamically sized)
N-th doc
Potential for many things to sweep lucene from that cache
xPlore Instance (fixed size) memory
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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 43
Some results of the query suite Test
Avg Resp MB preto cached consume all results (sec)
I/O per result
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
Total MB loaded into memory (cached + test)
10,970
• 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 44
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 45
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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