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Jakarta
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Performance Benchmarks
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The purpose of these user-submitted performance figures is to
give current and potential users of Lucene a sense
of how well Lucene scales. If the requirements for an upcoming
project is similar to an existing benchmark, you
will also have something to work with when designing the system
architecture for the application.
If you've conducted performance tests with Lucene, we'd
appreciate if you can submit these figures for display
on this page. Post these figures to the lucene-user mailing list
using this
template.
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User-submitted Benchmarks
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These benchmarks have been kindly submitted by Lucene users for
reference purposes.
We make NO guarantees regarding their accuracy or
validity.
We strongly recommend you conduct your own
performance benchmarks before deciding on a particular
hardware/software setup (and hopefully submit
these figures to us).
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Hamish Carpenter's benchmarks
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Hardware Environment
- Dedicated machine for indexing: yes
- CPU: Intel x86 P4 1.5Ghz
- RAM: 512 DDR
- Drive configuration: IDE 7200rpm Raid-1
Software environment
- Java Version: 1.3.1 IBM JITC Enabled
- Java VM:
- OS Version: Debian Linux 2.4.18-686
- Location of index: local
Lucene indexing variables
- Number of source documents: Random generator. Set
to make 1M documents
in 2x500,000 batches.
- Total filesize of source documents: > 1GB if
stored
- Average filesize of source documents: 1KB
- Source documents storage location: Filesystem
- File type of source documents: Generated
- Parser(s) used, if any:
- Analyzer(s) used: Default
- Number of fields per document: 11
- Type of fields: 1 date, 1 id, 9 text
- Index persistence: FSDirectory
Figures
- Time taken (in ms/s as an average of at least 3
indexing runs):
- Time taken / 1000 docs indexed: 49 seconds
- Memory consumption:
Notes
A windows client ran a random document generator which
created
documents based on some arrays of values and an excerpt
(approx 1kb)
from a text file of the bible (King James version).
These were submitted via a socket connection (open throughout
indexing process).
The index writer was not closed between index calls.
This created a 400Mb index in 23 files (after
optimization).
Query details:
Set up a threaded class to start x number of simultaneous
threads to
search the above created index.
Query: +Domain:sos +(+((Name:goo*^2.0 Name:plan*^2.0)
(Teaser:goo* Tea
ser:plan*) (Details:goo* Details:plan*)) -Cancel:y)
+DisplayStartDate:[mkwsw2jk0
-mq3dj1uq0] +EndDate:[mq3dj1uq0-ntlxuggw0]
This query counted 34000 documents and I limited the returned
documents
to 5.
This is using Peter Halacsy's IndexSearcherCache slightly
modified to
be a singleton returned cached searchers for a given
directory. This
solved an initial problem with too many files open and
running out of
linux handles for them.
Threads|Avg Time per query (ms)
1 1009ms
2 2043ms
3 3087ms
4 4045ms
.. .
.. .
10 10091ms
I removed the two date range terms from the query and it made
a HUGE
difference in performance. With 4 threads the avg time
dropped to 900ms!
Other query optimizations made little difference.
Hamish can be contacted at hamish at catalyst.net.nz.
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Justin Greene's benchmarks
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Hardware Environment
- Dedicated machine for indexing: No, but nominal
usage at time of indexing.
- CPU: Compaq Proliant 1850R/600 2 X pIII 600
- RAM: 1GB, 256MB allocated to JVM.
- Drive configuration: RAID 5 on Fibre Channel
Array
Software environment
- Java Version: 1.3.1_06
- Java VM:
- OS Version: Winnt 4/Sp6
- Location of index: local
Lucene indexing variables
- Number of source documents: about 60K
- Total filesize of source documents: 6.5GB
- Average filesize of source documents: 100K
(6.5GB/60K documents)
- Source documents storage location: filesystem on
NTFS
- File type of source documents:
- Parser(s) used, if any: Currently the only parser
used is the Quiotix html
parser.
- Analyzer(s) used: SimpleAnalyzer
- Number of fields per document: 8
- Type of fields: All strings, and all are stored
and indexed.
- Index persistence: FSDirectory
Figures
- Time taken (in ms/s as an average of at least 3
indexing runs): 1 hour 12 minutes, 1 hour 14 minutes and 1 hour 17
minutes. Note that the #
and size of documents changes daily.
- Time taken / 1000 docs indexed:
- Memory consumption: JVM is given 256MB and uses it
all.
Notes
We have 10 threads reading files from the filesystem and
parsing and
analyzing them and the pushing them onto a queue and a single
thread poping
them from the queue and indexing. Note that we are indexing
email messages
and are storing the entire plaintext in of the message in the
index. If the
message contains attachment and we do not have a filter for
the attachment
(ie. we do not do PDFs yet), we discard the data.
Justin can be contacted at tvxh-lw4x at spamex.com.
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Daniel Armbrust's benchmarks
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My disclaimer is that this is a very poor "Benchmark". It was not done for raw speed,
nor was the total index built in one shot. The index was created on several different
machines (all with these specs, or very similar), with each machine indexing batches of 500,000 to
1 million documents per batch. Each of these small indexes was then moved to a
much larger drive, where they were all merged together into a big index.
This process was done manually, over the course of several months, as the sources became available.
Hardware Environment
- Dedicated machine for indexing: no - The machine had moderate to low load. However, the indexing process was built single
threaded, so it only took advantage of 1 of the processors. It usually got 100% of this processor.
- CPU: Sun Ultra 80 4 x 64 bit processors
- RAM: 4 GB Memory
- Drive configuration: Ultra-SCSI Wide 10000 RPM 36GB Drive
Software environment
- Java Version: 1.3.1
- Java VM:
- OS Version: Sun 5.8 (64 bit)
- Location of index: local
Lucene indexing variables
- Number of source documents: 13,820,517
- Total filesize of source documents: 87.3 GB
- Average filesize of source documents: 6.3 KB
- Source documents storage location: Filesystem
- File type of source documents: XML
- Parser(s) used, if any:
- Analyzer(s) used: A home grown analyzer that simply removes stopwords.
- Number of fields per document: 1 - 31
- Type of fields: All text, though 2 of them are dates (20001205) that we filter on
- Index persistence: FSDirectory
- Index size: 12.5 GB
Figures
- Time taken (in ms/s as an average of at least 3
indexing runs): For 617271 documents, 209698 seconds (or ~2.5 days)
- Time taken / 1000 docs indexed: 340 Seconds
- Memory consumption: (java executed with) java -Xmx1000m -Xss8192k so
1 GB of memory was allotted to the indexer
Notes
The source documents were XML. The "indexer" opened each document one at a time, ran an
XSL transformation on them, and then proceeded to index the stream. The indexer optimized
the index every 50,000 documents (on this run) though previously, we optimized every
300,000 documents. The performance didn't change much either way. We did no other
tuning (RAM Directories, separate process to pretransform the source material, etc)
to make it index faster. When all of these individual indexes were built, they were
merged together into the main index. That process usually took ~ a day.
Daniel can be contacted at Armbrust.Daniel at mayo.edu.
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Geoffrey Peddle's benchmarks
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I'm doing a technical evaluation of search engines
for Ariba, an enterprise application software company.
I compared Lucene to a commercial C language based
search engine which I'll refer to as vendor A.
Overall Lucene's performance was similar to vendor A
and met our application's requirements. I've
summarized our results below.
Search scalability:
We ran a set of 16 queries in a single thread for 20
iterations. We report below the times for the last 15
iterations (ie after the system was warmed up). The
4 sets of results below are for indexes with between
50,000 documents to 600,000 documents. Although the
times for Lucene grew faster with document count than
vendor A they were comparable.
50K documents
Lucene 5.2 seconds
A 7.2
200K
Lucene 15.3
A 15.2
400K
Lucene 28.2
A 25.5
600K
Lucene 41
A 33
Individual Query times:
Total query times are very similar between the 2
systems but there were larger differences when you
looked at individual queries.
For simple queries with small result sets Vendor A was
consistently faster than Lucene. For example a
single query might take vendor A 32 thousands of a
second and Lucene 64 thousands of a second. Both
times are however well within acceptable response
times for our application.
For simple queries with large result sets Vendor A was
consistently slower than Lucene. For example a
single query might take vendor A 300 thousands of a
second and Lucene 200 thousands of a second.
For more complex queries of the form (term1 or term2
or term3) AND (term4 or term5 or term6) AND (term7 or
term8) the results were more divergent. For
queries with small result sets Vendor A generally had
very short response times and sometimes Lucene had
significantly larger response times. For example
Vendor A might take 16 thousands of a second and
Lucene might take 156. I do not consider it to be
the case that Lucene's response time grew unexpectedly
but rather that Vendor A appeared to be taking
advantage of an optimization which Lucene didn't have.
(I believe there's been discussions on the dev
mailing list on complex queries of this sort.)
Index Size:
For our test data the size of both indexes grew
linearly with the number of documents. Note that
these sizes are compact sizes, not maximum size during
index loading. The numbers below are from running du
-k in the directory containing the index data. The
larger number's below for Vendor A may be because it
supports additional functionality not available in
Lucene. I think it's the constant rate of growth
rather than the absolute amount which is more
important.
50K documents
Lucene 45516 K
A 63921
200K
Lucene 171565
A 228370
400K
Lucene 345717
A 457843
600K
Lucene 511338
A 684913
Indexing Times:
These times are for reading the documents from our
database, processing them, inserting them into the
document search product and index compacting. Our
data has a large number of fields/attributes. For
this test I restricted Lucene to 24 attributes to
reduce the number of files created. Doing this I was
able to specify a merge width for Lucene of 60. I
found in general that Lucene indexing performance to
be very sensitive to changes in the merge width.
Note also that our application does a full compaction
after inserting every 20,000 documents. These times
are just within our acceptable limits but we are
interested in alternatives to increase Lucene's
performance in this area.
600K documents
Lucene 81 minutes
A 34 minutes
(I don't have accurate results for all sizes on this
measure but believe that the indexing time for both
solutions grew essentially linearly with size. The
time to compact the index generally grew with index
size but it's a small percent of overall time at these
sizes.)
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