Apache
/
lucene
mirror of https://github.com/apache/lucene.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

- Updated with Hamish Carpenter's benchmarks, thanks for integrating this, Kelvin 

git-svn-id: https://svn.apache.org/repos/asf/lucene/java/trunk@150175 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Otis Gospodnetic 2004-01-26 18:23:55 +00:00
parent 0a39c6131b
commit 7845fcc2af
2 changed files with 358 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

190
docs/benchmarks.html
View File

@ -278,7 +278,6 @@
</p>
<p>
<b>Notes</b><br />
<li><i>Notes</i>:
<p>
A windows client ran a random document generator which
created
@ -336,7 +335,7 @@
difference in performance. With 4 threads the avg time
dropped to 900ms!
</p>
<p>Other query optimizations made little difference.</p></li>
<p>Other query optimizations made little difference.</p>
</p>
</ul>
<p>
@ -401,7 +400,6 @@
</p>
<p>
<b>Notes</b><br />
<li><i>Notes</i>:
<p>
We have 10 threads reading files from the filesystem and
parsing and
@ -414,7 +412,7 @@
message contains attachment and we do not have a filter for
the attachment
(ie. we do not do PDFs yet), we discard the data.
</p></li>
</p>
</p>
</ul>
<p>
@ -480,7 +478,6 @@
</p>
<p>
<b>Notes</b><br />
<li><i>Notes</i>:
<p>
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
@ -489,7 +486,7 @@
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.
</p></li>
</p>
</p>
</ul>
<p>
@ -498,6 +495,187 @@
</blockquote>
</td></tr>
<tr><td><br/></td></tr>
</table>
<table border="0" cellspacing="0" cellpadding="2" width="100%">
<tr><td bgcolor="#828DA6">
<font color="#ffffff" face="arial,helvetica,sanserif">
<a name="Geoffrey Peddle's benchmarks"><strong>Geoffrey Peddle's benchmarks</strong></a>
</font>
</td></tr>
<tr><td>
<blockquote>
<p>
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.
</p>
<p>
Search scalability:<br />
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.
</p>
<pre>
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
</pre>
<p>
Individual Query times:<br />
Total query times are very similar between the 2
systems but there were larger differences when you
looked at individual queries.
</p>
<p>
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.
</p>
<p>
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.)
</p>
<p>
Index Size:<br />
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.
</p>
<pre>
50K documents
Lucene 45516 K
A 63921
200K
Lucene 171565
A 228370
400K
Lucene 345717
A 457843
600K
Lucene 511338
A 684913
</pre>
<p>
Indexing Times:<br />
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.
</p>
<p>
<pre>
600K documents
Lucene 81 minutes
A 34 minutes
</pre>
</p>
<p>
(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.)
</p>
<ul>
<p>
<b>Hardware Environment</b><br />
<li><i>Dedicated machine for indexing</i>: yes</li>
<li><i>CPU</i>: Dell Pentium 4 CPU 2.00Ghz, 1cpu</li>
<li><i>RAM</i>: 1 GB Memory</li>
<li><i>Drive configuration</i>: Fujitsu MAM3367MP SCSI </li>
</p>
<p>
<b>Software environment</b><br />
<li><i>Java Version</i>: 1.4.2_02</li>
<li><i>Java VM</i>: JDK</li>
<li><i>OS Version</i>: Windows XP </li>
<li><i>Location of index</i>: local</li>
</p>
<p>
<b>Lucene indexing variables</b><br />
<li><i>Number of source documents</i>: 600,000</li>
<li><i>Total filesize of source documents</i>: from database</li>
<li><i>Average filesize of source documents</i>: from database</li>
<li><i>Source documents storage location</i>: from database</li>
<li><i>File type of source documents</i>: XML</li>
<li><i>Parser(s) used, if any</i>: </li>
<li><i>Analyzer(s) used</i>: small variation on WhitespaceAnalyzer</li>
<li><i>Number of fields per document</i>: 24</li>
<li><i>Type of fields</i>: A1 keyword, 1 big unindexed, rest are unstored and a mix of tokenized/untokenized</li>
<li><i>Index persistence</i>: FSDirectory</li>
<li><i>Index size</i>: 12.5 GB</li>
</p>
<p>
<b>Figures</b><br />
<li><i>Time taken (in ms/s as an average of at least 3
indexing runs)</i>: 600,000 documents in 81 minutes (du -k = 511338)</li>
<li><i>Time taken / 1000 docs indexed</i>: 123 documents/second</li>
<li><i>Memory consumption</i>: -ms256m -mx512m -Xss4m -XX:MaxPermSize=512M</li>
</p>
<p>
<b>Notes</b><br />
<p>
<li>merge width of 60</li>
<li>did a compact every 20,000 documents</li>
</p>
</p>
</ul>
</blockquote>
</td></tr>
<tr><td><br/></td></tr>
</table>
</blockquote>
</p>

181
xdocs/benchmarks.xml
View File

@ -141,7 +141,6 @@
</p>
<p>
<b>Notes</b><br/>
<li><i>Notes</i>:
<p>
A windows client ran a random document generator which
created
@ -199,7 +198,7 @@
difference in performance. With 4 threads the avg time
dropped to 900ms!
</p>
<p>Other query optimizations made little difference.</p></li>
<p>Other query optimizations made little difference.</p>
</p>
</ul>
<p>
@ -255,7 +254,6 @@
</p>
<p>
<b>Notes</b><br/>
<li><i>Notes</i>:
<p>
We have 10 threads reading files from the filesystem and
parsing and
@ -268,7 +266,7 @@
message contains attachment and we do not have a filter for
the attachment
(ie. we do not do PDFs yet), we discard the data.
</p></li>
</p>
</p>
</ul>
<p>
@ -326,7 +324,6 @@
</p>
<p>
<b>Notes</b><br/>
<li><i>Notes</i>:
<p>
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
@ -335,14 +332,184 @@
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.
</p></li>
</p>
</p>
</ul>
<p>
Daniel can be contacted at Armbrust.Daniel at mayo.edu.
</p>
</subsection>
<subsection name="Geoffrey Peddle's benchmarks">
<p>
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.
</p>
<p>
Search scalability:<br/>
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.
</p>
<pre>
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
</pre>
<p>
Individual Query times:<br/>
Total query times are very similar between the 2
systems but there were larger differences when you
looked at individual queries.
</p>
<p>
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.
</p>
<p>
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.)
</p>
<p>
Index Size:<br/>
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.
</p>
<pre>
50K documents
Lucene 45516 K
A 63921
200K
Lucene 171565
A 228370
400K
Lucene 345717
A 457843
600K
Lucene 511338
A 684913
</pre>
<p>
Indexing Times:<br/>
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.
</p>
<p>
<pre>
600K documents
Lucene 81 minutes
A 34 minutes
</pre>
</p>
<p>
(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.)
</p>
<ul>
<p>
<b>Hardware Environment</b><br/>
<li><i>Dedicated machine for indexing</i>: yes</li>
<li><i>CPU</i>: Dell Pentium 4 CPU 2.00Ghz, 1cpu</li>
<li><i>RAM</i>: 1 GB Memory</li>
<li><i>Drive configuration</i>: Fujitsu MAM3367MP SCSI </li>
</p>
<p>
<b>Software environment</b><br/>
<li><i>Java Version</i>: 1.4.2_02</li>
<li><i>Java VM</i>: JDK</li>
<li><i>OS Version</i>: Windows XP </li>
<li><i>Location of index</i>: local</li>
</p>
<p>
<b>Lucene indexing variables</b><br/>
<li><i>Number of source documents</i>: 600,000</li>
<li><i>Total filesize of source documents</i>: from database</li>
<li><i>Average filesize of source documents</i>: from database</li>
<li><i>Source documents storage location</i>: from database</li>
<li><i>File type of source documents</i>: XML</li>
<li><i>Parser(s) used, if any</i>: </li>
<li><i>Analyzer(s) used</i>: small variation on WhitespaceAnalyzer</li>
<li><i>Number of fields per document</i>: 24</li>
<li><i>Type of fields</i>: A1 keyword, 1 big unindexed, rest are unstored and a mix of tokenized/untokenized</li>
<li><i>Index persistence</i>: FSDirectory</li>
<li><i>Index size</i>: 12.5 GB</li>
</p>
<p>
<b>Figures</b><br/>
<li><i>Time taken (in ms/s as an average of at least 3
indexing runs)</i>: 600,000 documents in 81 minutes (du -k = 511338)</li>
<li><i>Time taken / 1000 docs indexed</i>: 123 documents/second</li>
<li><i>Memory consumption</i>: -ms256m -mx512m -Xss4m -XX:MaxPermSize=512M</li>
</p>
<p>
<b>Notes</b><br/>
<p>
<li>merge width of 60</li>
<li>did a compact every 20,000 documents</li>
</p>
</p>
</ul>
</subsection>
</section>
</body>