enhancing the performance and extensibility of the xc metadataservicestoolkit ben anderson, software...
TRANSCRIPT
Enhancing the Performance and Extensibility of the XC
MetadataServicesToolkitBen Anderson, Software Engineer, XCO
Download this presentation:www.extensiblecatalog.org/learnmore
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Timeline
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Jennifer Bowenpresented at
code4lib2/10
I began at XCO3/10
work beganon 0.34/10
0.3 released1/11
0.2 released
1.0 released
MARCXML(6M records) DC-TERMS
(13k records)
XC Software ComponentsUser Interface for searching and browsing
Library Website (on Drupal)Library Website (on Drupal)
Integrated Library SystemIntegrated Library System RepositoryRepository
XC Drupal Toolkit
Tools for automated processing of large batches of metadata XC Metadata
Services ToolkitXC Metadata
Services Toolkit
Tools for connectivity between XC and an ILS
XC
Circ
. Sta
tus/
Req.
Auth
entic
ation
XC NCIP Toolkit
XC NCIP Toolkit
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XC OAI ToolkitXC OAI Toolkit irplus
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Learn More about XC atwww.extensiblecatalog.org
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One Example of Process Flow
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MARC BIBrecord from externalrepository
NormalizedMARC BIBrecord from normalization service
FRBRized recordsfrom transformationservice
work
expression
manifestation
M S T
Logical Process
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OAI-PMH Harvest
MARCNormalization
Service
MARC-XCTransformation
Service
Pseudo OAI-PMH Harvests
OAI-PMH Harvestable
provider caches
repo repo
Add an External Repository
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Schedule a Harvest
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Configure Processing Rules
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Browse Records
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Goals for 0.3
• Each service should process one million records per hour on an “average library server”– 1.5 GHz SPARC V9 – 8G RAM (3G for the JVM)– 10k RPM hard drive
• Services should have little to no degradation as the size of a repository grows– University of Rochester has 6M records
• Implementing a service should be easy– it should require no knowledge of MST internals– it should not be up to the service implementer to figure
out how to build and package their service
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Determine Throughput of 0.2
• Using the MARC Normalization service as our metric, the first million records processed at average at a speed of:– 29 ms/record = 120k/hr (goal is 3.6 ms/rec = 1M/hr)
• Before the service processed 2 million records, the process crawled to a halt (goal was little to no degradation of at least 6 million records).
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Determine Bottlenecks with TimingLogger
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This codeproduces this output
Bottleneck Breakdown
• 29 ms per record– 2.5 ms to create DOM– 5 ms for actual service processing (the innards of
the MARC-Normalization service)– 21 ms for querying solr and inserting
• This is the average - both querying and inserting are done in batch.
• I had a hard time separating the two
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0.2 Design
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•All data needed for the UI•except for searching and browsing records
•All data needed for configuring harvests, services, processing rules, etc
•Text indexes necessary for searching and browsing records•All record/repository data
0.3 Design Change to use MySQL
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•All data needed for the UI•except for searching and browsing records
•All data needed for configuring harvests, services, processing rules, etc•All record/repository data
•Doesn’t store any data•Use only for indexing records to support searching in the UI
0.3 Design – Keep the table sizes small
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One index for allrepositories
Each external repository cache and each service gets its own set of database tables
externalprovider
reponormalization
repotransformation
repo
one or moreper record
zero or moreper record
one per record
0.3 Design - Yes, a boring ERD
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record_updates
record_id
update_date
records_xml
record_id
xml
record_sets
records_xml
record_id
xml
record_predecessors
record_id
pred_record_id
Did that improve things?
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• 11 ms per record (previously 29)– 2.5 ms to create DOM– 5 ms for actual service processing (the innards of the
MARC-Normalization service)– 3.5 ms (previously 21) for querying MySQL and
inserting into MySQL• again, both querying and inserting are done in batch• The query time is almost nill - it’s the inserting that takes
time.• It’s faster, but still nearly 3x slower than our goal• The performance showed little to no degradation
Get rid of XPath
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XPath isn’t a bad technology, but when you’re optimizing for performance, it can be beneficial to find other ways to accomplish the same task. So, I changed this code…
to this code…
Did that improve things?
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• 7 ms per record (previously 11)– 2.5 ms to create DOM– 1.0 ms (previously 5) for actual service processing
(the innards of the MARC-Normalization service)– 3.5 ms for MySQL inserts
• It’s faster, but still nearly 2x slower than our goal
Delayed Indexing in MySQL
• MySQL modifies table indexes with each insert.
• It is faster to the drop indexes, insert lots of rows into the tables, and then add the indexes back.– This is the way mysqldump works– This means you can’t read the data while doing an
insert. No big deal – we’ll just do it during large loads.
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Did that improve things?
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• 6 ms per record (previously 11)– 2.5 ms to create DOM– 1.0 ms for actual service processing (the innards of
the MARC-Normalization service)– 2.2 ms (previously 3.5) for MySQL inserts
• It’s faster, but still nearly 2x slower than our goal
Batch Prepared Statements
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Java/JDBC provides an extremely highly performant method for sending large chunks of data to the db at once using batch prepared statements.
There’s no way to speed this part up… or so I thought…
LOAD DATA INFILE
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When discussing db optimizations with XC’s Drupal Toolkit developer, Peter Kiraly, he said PHP didn’t have the same ability. Instead he’d have to write out a csv file and load that in. I figured I might as well try it.
Did that improve things?
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• 4 ms per record (previously 6)– 2.5 ms to create DOM– 1.0 ms for actual service processing (the innards of
the MARC-Normalization service)– 0.6 ms (previously 2.2) for MySQL inserts
• Pretty close, but still not there
Sometimes it’s the little things
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DomFactoryBuilderDOAServiceFactoryFactoryImplI knew enough not to create the DocumentBuilderFactory each time, but didn’t realize creating the DocumentBuilder each time would have that much of an effect.
Code was
Code is now
Did that improve things?
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• 3 ms per record (previously 4)– 0.9 ms (previously 2.5) to create DOM– 1.0 ms for actual service processing (the innards of
the MARC-Normalization service)– 0.6 ms for MySQL inserts
• WE DID IT! We have exceeded our goal!
0.2 Service Development
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Internals of the MST were exposed to the service developer and the developer was expected to re-implement much of this internal code.
code.google.com/p/xcmetadataservicestoolkit/
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0.3.x Service Development
• Install Java, Ant, MySQL
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$ wget 'http://xcmetadataservicestoolkit.googlecode.com/files/example-0.3.0-dev-env.zip’
$ unzip example-0.3.0-dev-env.zip$ cd example$ ant retrieve$ ant -Dtest=ProcessFiles test$ ls -ladh ./build/test/actual_output_records/1/*$ ant zip
Input Files for Testing
$ ls -1 ./test/input_records/1/* | xargs cat<records xmlns="http://www.openarchives.org/OAI/2.0/"> <record> <header> <identifier>oai:mst.rochester.edu:bib:1</identifier> </header> <metadata> <foo xmlns="foo:bar">pb&j</foo> </metadata> </record> <record> <header> <identifier>oai:mst.rochester.edu:bib:1</identifier> </header> <metadata> <foo xmlns="foo:bar">pb&j 2</foo> </metadata> </record></records>...
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Output Files from Testing
$ ls -1 ./build/test/actual_output_records/1/* | xargs cat<records xmlns="http://www.openarchives.org/OAI/2.0/"><record> <header status="replaced"> <identifier>oai:mst.rochester.edu:example/1</identifier> <datestamp /> <predecessors> <predecessor>oai:mst.rochester.edu:bib:1</predecessor> </predecessors> </header> <metadata> <foo xmlns="foo:bar"> pb&j <bar>you've been foobarred!</bar> </foo> </metadata></record>
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Implementing in Code
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More tidbits for interested implementers
• The MST now is configured via spring– each service is given it’s own application context
as well as it’s own classloader• This means it can use all the objects and services from
the MST while not worrying about name collisions (naming or dependencies) w/ other services
• Each service is given it’s own db schema (again, so you don’t have to worry about name collisions). The db schema is prefixed w/ “xc_”
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Other Services
• MARC-XC-Transformation Just as fast as the marcnormalization service
• DC-XC-Transformation Initially contributed by Kyushu University (in Japan) – now one of our core services.
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Photo Credits
• All photos taken from flickr.com– “Brick Wall” by somenametoforget– “Snail” by DRB62– “Paris Train” by Pictr 30D– “Spaghetti with tomato sauce” by HatM– “Hawk in Flight” by Nick Chill– “Tortoise” by GraphicReality
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Final Numbers
0.2• 125k records / hr
29 ms / record
• fell down before 2M records processed
• not easily extensible40
0.3• 1.2M records / hr
3.0 ms / record
• processed 16M records with no degradation
• easily extensible
1.5 GHz CPU