lucene

Benchmarking Lucene By Tasks.

This package provides "task based" performance benchmarking of Lucene. One can use the predefined benchmarks, or create new ones.

Contained packages:

Package	Description
stats	Statistics maintained when running benchmark tasks.
tasks	Benchmark tasks.
feeds	Sources for benchmark inputs: documents and queries.
utils	Utilities used for the benchmark, and for the reports.
programmatic	Sample performance test written programatically.

Benchmarking By Tasks
How to use
Benchmark "algorithm"
Supported tasks/commands
Benchmark properties
Example input algorithm and the result benchmark report.
Results record counting clarified

Benchmarking By Tasks

Benchmark Lucene using task primitives.

A benchmark is composed of some predefined tasks, allowing for creating an index, adding documents, optimizing, searching, generating reports, and more. A benchmark run takes an "algorithm" file that contains a description of the sequence of tasks making up the run, and some properties defining a few additional characteristics of the benchmark run.

How to use

Easiest way to run a benchmarks is using the predefined ant task:

ant run-task
- would run the micro-standard.alg "algorithm".
ant run-task -Dtask.alg=conf/compound-penalty.alg
- would run the compound-penalty.alg "algorithm".
ant run-task -Dtask.alg=[full-path-to-your-alg-file]
- would run your perf test "algorithm".
java org.apache.lucene.benchmark.byTask.programmatic.Sample
- would run a performance test programmatically - without using an alg file. This is less readable, and less convinient, but possible.

You may find existing tasks sufficient for defining the benchmark you need, otherwise, you can extend the framework to meet your needs, as explained herein.

Each benchmark run has a DocMaker and a QueryMaker. These two should usually match, so that "meaningful" queries are used for a certain collection. Properties set at the header of the alg file define which "makers" should be used. You can also specify your own makers, implementing the DocMaker and QureyMaker interfaces.

Benchmark .alg file contains the benchmark "algorithm". The syntax is described below. Within the algorithm, you can specify groups of commands, assign them names, specify commands that should be repeated, do commands in serial or in parallel, and also control the speed of "firing" the commands.

This allows, for instance, to specify that an index should be opened for update, documents should be added to it one by one but not faster than 20 docs a minute, and, in parallel with this, some N queries should be searched against that index, again, no more than 2 queries a second. You can have the searches all share an index reader, or have them each open its own reader and close it afterwords.

If the commands available for use in the algorithm do not meet your needs, you can add commands by adding a new task under org.apache.lucene.benchmark.byTask.tasks - you should extend the PerfTask abstract class. Make sure that your new task class name is suffixed by Task. Assume you added the class "WonderfulTask" - doing so also enables the command "Wonderful" to be used in the algorithm.

External classes: It is sometimes useful to invoke the benchmark package with your external alg file that configures the use of your own doc/query maker and or html parser. You can work this out without modifying the benchmark package code, by passing your class path with the benchmark.ext.classpath property:

ant run-task -Dtask.alg=[full-path-to-your-alg-file] -Dbenchmark.ext.classpath=/mydir/classes -Dtask.mem=512M

Benchmark "algorithm"

The following is an informal description of the supported syntax.

Measuring: When a command is executed, statistics for the elapsed execution time and memory consumption are collected. At any time, those statistics can be printed, using one of the available ReportTasks.
Comments start with '#'.
Serial sequences are enclosed within '{ }'.
Parallel sequences are enclosed within '[ ]'
Sequence naming: To name a sequence, put '"name"' just after '{' or '['.
Example - { "ManyAdds" AddDoc } : 1000000 - would name the sequence of 1M add docs "ManyAdds", and this name would later appear in statistic reports. If you don't specify a name for a sequence, it is given one: you can see it as the algorithm is printed just before benchmark execution starts.
Repeating: To repeat sequence tasks N times, add ': N' just after the sequence closing tag - '}' or ']' or '>'.
Example - [ AddDoc ] : 4 - would do 4 addDoc in parallel, spawning 4 threads at once.
Example - [ AddDoc AddDoc ] : 4 - would do 8 addDoc in parallel, spawning 8 threads at once.
Example - { AddDoc } : 30 - would do addDoc 30 times in a row.
Example - { AddDoc AddDoc } : 30 - would do addDoc 60 times in a row.
Exhaustive repeating: use * instead of a number to repeat exhaustively. This is sometimes useful, for adding as many files as a doc maker can create, without iterating over the same file again, especially when the exact number of documents is not known in advance. For insance, TREC files extracted from a zip file. Note: when using this, you must also set doc.maker.forever to false.
Example - { AddDoc } : * - would add docs until the doc maker is "exhausted".
Command parameter: a command can optionally take a single parameter. If the certain command does not support a parameter, or if the parameter is of the wrong type, reading the algorithm will fail with an exception and the test would not start. Currently the following tasks take optional parameters:
- AddDoc takes a numeric parameter, indicating the required size of added document. Note: if the DocMaker implementation used in the test does not support makeDoc(size), an exception would be thrown and the test would fail.
- DeleteDoc takes numeric parameter, indicating the docid to be deleted. The latter is not very useful for loops, since the docid is fixed, so for deletion in loops it is better to use the doc.delete.step property.
- SetProp takes a name,value mandatory param, ',' used as a separator.
Example - AddDoc(2000) - would add a document of size 2000 (~bytes). See conf/task-sample.alg for how this can be used, for instance, to check which is faster, adding many smaller documents, or few larger documents. Next candidates for supporting a parameter may be the Search tasks, for controlling the qurey size.


 
 Statistic recording elimination: - a sequence can also end with
 '>',
 in which case child tasks would not store their statistics.
 This can be useful to avoid exploding stats data, for adding say 1M docs.
 
Example - { "ManyAdds" AddDoc > : 1000000 -
 would add million docs, measure that total, but not save stats for each addDoc.
 
Notice that the granularity of System.currentTimeMillis() (which is used
 here) is system dependant,
 and in some systems an operation that takes 5 ms to complete may show 0 ms
 latency time in performance measurements.
 Therefore it is sometimes more accurate to look at the elapsed time of a larger
 sequence, as demonstrated here.
 
 
 Rate:
 To set a rate (ops/sec or ops/min) for a sequence, add
 ': N : R' just after sequence closing tag.
 This would specify repetition of N with rate of R operations/sec.
 Use 'R/sec' or
 'R/min'
 to explicitely specify that the rate is per second or per minute.
 The default is per second,
 
Example -  [ AddDoc ] : 400 : 3 - would do 400
 addDoc in parallel, starting up to 3 threads per second.
 
Example -  { AddDoc } : 100 : 200/min - would
 do 100 addDoc serially,
 waiting before starting next add, if otherwise rate would exceed 200 adds/min.
 
 
 Command names: Each class "AnyNameTask" in the
 package org.apache.lucene.benchmark.byTask.tasks,
 that extends PerfTask, is supported as command "AnyName" that can be
 used in the benchmark "algorithm" description.
 This allows to add new commands by just adding such classes.





Supported tasks/commands


Existing tasks can be divided into a few groups:
regular index/search work tasks, report tasks, and control tasks.




 
 Report tasks: There are a few Report commands for generating reports.
 Only task runs that were completed are reported.
 (The 'Report tasks' themselves are not measured and not reported.)
 
             
            RepAll - all (completed) task runs.
            
            
            RepSumByName - all statistics,
            aggregated by name. So, if AddDoc was executed 2000 times,
            only 1 report line would be created for it, aggregating all those
            2000 statistic records.
            
            
            RepSelectByPref   prefixWord - all
            records for tasks whose name start with
            prefixWord.
            
            
            RepSumByPref   prefixWord - all
            records for tasks whose name start with
            prefixWord,
            aggregated by their full task name.
            
            
            RepSumByNameRound - all statistics,
            aggregated by name and by Round.
            So, if AddDoc was executed 2000 times in each of 3
            rounds, 3 report lines would be
            created for it,
            aggregating all those 2000 statistic records in each round.
            See more about rounds in the NewRound
            command description below.
            
            
            RepSumByPrefRound   prefixWord -
            similar to RepSumByNameRound,
            just that only tasks whose name starts with
            prefixWord are included.
            
 
 If needed, additional reports can be added by extending the abstract class
 ReportTask, and by
 manipulating the statistics data in Points and TaskStats.
 

 Control tasks: Few of the tasks control the benchmark algorithm
 all over:
 
     
     ClearStats - clears the entire statistics.
     Further reports would only include task runs that would start after this
     call.
     
     
     NewRound - virtually start a new round of
     performance test.
     Although this command can be placed anywhere, it mostly makes sense at
     the end of an outermost sequence.
     
This increments a global "round counter". All task runs that
     would start now would
     record the new, updated round counter as their round number.
     This would appear in reports.
     In particular, see RepSumByNameRound above.
     
An additional effect of NewRound, is that numeric and boolean
     properties defined (at the head
     of the .alg file) as a sequence of values, e.g. 
     merge.factor=mrg:10:100:10:100 would
     increment (cyclic) to the next value.
     Note: this would also be reflected in the reports, in this case under a
     column that would be named "mrg".
     
     
     ResetInputs - DocMaker and the
     various QueryMakers
     would reset their counters to start.
     The way these Maker interfaces work, each call for makeDocument()
     or makeQuery() creates the next document or query
     that it "knows" to create.
     If that pool is "exhausted", the "maker" start over again.
     The resetInpus command
     therefore allows to make the rounds comparable.
     It is therefore useful to invoke ResetInputs together with NewRound.
     
     
     ResetSystemErase - reset all index
     and input data and call gc.
     Does NOT reset statistics. This contains ResetInputs.
     All writers/readers are nullified, deleted, closed.
     Index is erased.
     Directory is erased.
     You would have to call CreateIndex once this was called...
     
     
     ResetSystemSoft -  reset all
     index and input data and call gc.
     Does NOT reset statistics. This contains ResetInputs.
     All writers/readers are nullified, closed.
     Index is NOT erased.
     Directory is NOT erased.
     This is useful for testing performance on an existing index,
     for instance if the construction of a large index
     took a very long time and now you would to test
     its search or update performance.
     
 
 

 
 Other existing tasks are quite straightforward and would
 just be briefly described here.
 
     
     CreateIndex and
     OpenIndex both leave the
     index open for later update operations.
     CloseIndex would close it.
     
     
     OpenReader, similarly, would
     leave an index reader open for later search operations.
     But this have further semantics.
     If a Read operation is performed, and an open reader exists,
     it would be used.
     Otherwise, the read operation would open its own reader
     and close it when the read operation is done.
     This allows testing various scenarios - sharing a reader,
     searching with "cold" reader, with "warmed" reader, etc.
     The read operations affected by this are:
     Warm,
     Search,
     SearchTrav (search and traverse),
     and SearchTravRet (search
     and traverse and retrieve).
     Notice that each of the 3 search task types maintains
     its own queryMaker instance.
     
 
 


Benchmark properties


Properties are read from the header of the .alg file, and
define several parameters of the performance test.
As mentioned above for the NewRound task,
numeric and boolean properties that are defined as a sequence
of values, e.g. merge.factor=mrg:10:100:10:100
would increment (cyclic) to the next value,
when NewRound is called, and would also
appear as a named column in the reports (column
name would be "mrg" in this example).



Some of the currently defined properties are:



    
    analyzer - full
    class name for the analyzer to use.
    Same analyzer would be used in the entire test.
    

    
    directory - valid values are
    This tells which directory to use for the performance test.
    

    
    Index work parameters:
    Multi int/boolean values would be iterated with calls to NewRound.
    There would be also added as columns in the reports, first string in the
    sequence is the column name.
    (Make sure it is no shorter than any value in the sequence).
    
        max.buffered
        
Example: max.buffered=buf:10:10:100:100 -
        this would define using maxBufferedDocs of 10 in iterations 0 and 1,
        and 100 in iterations 2 and 3.
        
        
        merge.factor - which
        merge factor to use.
        
        
        compound - whether the index is
        using the compound format or not. Valid values are "true" and "false".
        
    



Here is a list of currently defined properties:



  Root directory for data and indexes:
    work.dir (default is System property "benchmark.work.dir" or "work".)
    
  

  Docs and queries creation:
    analyzer
    
doc.maker
    
doc.maker.forever
    
html.parser
    
doc.stored
    
doc.tokenized
    
doc.term.vector
    
doc.term.vector.positions
    
doc.term.vector.offsets
    
doc.store.body.bytes
    
docs.dir
    
query.maker
    
file.query.maker.file
    
file.query.maker.default.field
    
  

  Logging:
    doc.add.log.step
    
doc.delete.log.step
    
log.queries
    
task.max.depth.log
    
doc.tokenize.log.step
    
  

  Index writing:
    compound
    
merge.factor
    
max.buffered
    
directory
    
ram.flush.mb
    
autocommit
    
  

  Doc deletion:
    doc.delete.step
    
  




For sample use of these properties see the *.alg files under conf.



Example input algorithm and the result benchmark report

The following example is in conf/sample.alg:
# --------------------------------------------------------
#
# Sample: what is the effect of doc size on indexing time?
#
# There are two parts in this test:
# - PopulateShort adds 2N documents of length  L
# - PopulateLong  adds  N documents of length 2L
# Which one would be faster?
# The comparison is done twice.
#
# --------------------------------------------------------

# -------------------------------------------------------------------------------------
# multi val params are iterated by NewRound's, added to reports, start with column name.
merge.factor=mrg:10:20
max.buffered=buf:100:1000
compound=true

analyzer=org.apache.lucene.analysis.standard.StandardAnalyzer
directory=FSDirectory

doc.stored=true
doc.tokenized=true
doc.term.vector=false
doc.add.log.step=500

docs.dir=reuters-out

doc.maker=org.apache.lucene.benchmark.byTask.feeds.SimpleDocMaker

query.maker=org.apache.lucene.benchmark.byTask.feeds.SimpleQueryMaker

# task at this depth or less would print when they start
task.max.depth.log=2

log.queries=false
# -------------------------------------------------------------------------------------
{

    { "PopulateShort"
        CreateIndex
        { AddDoc(4000) > : 20000
        Optimize
        CloseIndex
    >

    ResetSystemErase

    { "PopulateLong"
        CreateIndex
        { AddDoc(8000) > : 10000
        Optimize
        CloseIndex
    >

    ResetSystemErase

    NewRound

} : 2

RepSumByName
RepSelectByPref Populate





The command line for running this sample:

ant run-task -Dtask.alg=conf/sample.alg



The output report from running this test contains the following:
Operation     round mrg  buf   runCnt   recsPerRun        rec/s  elapsedSec    avgUsedMem    avgTotalMem
PopulateShort     0  10  100        1        20003        119.6      167.26    12,959,120     14,241,792
PopulateLong -  - 0  10  100 -  -   1 -  -   10003 -  -  - 74.3 -  - 134.57 -  17,085,208 -   20,635,648
PopulateShort     1  20 1000        1        20003        143.5      139.39    63,982,040     94,756,864
PopulateLong -  - 1  20 1000 -  -   1 -  -   10003 -  -  - 77.0 -  - 129.92 -  87,309,608 -  100,831,232




Results record counting clarified

Two columns in the results table indicate records counts: records-per-run and
records-per-second. What does it mean?

Almost every task gets 1 in this count just for being executed.
Task sequences aggregate the counts of their child tasks,
plus their own count of 1.
So, a task sequence containing 5 other task sequences, each running a single
other task 10 times, would have a count of 1 + 5 * (1 + 10) = 56.

The traverse and retrieve tasks "count" more: a traverse task
would add 1 for each traversed result (hit), and a retrieve task would
additionally add 1 for each retrieved doc. So, regular Search would
count 1, SearchTrav that traverses 10 hits would count 11, and a
SearchTravRet task that retrieves (and traverses) 10, would count 21.

Confusing? this might help: always examine the elapsedSec column,
and always compare "apples to apples", .i.e. it is interesting to check how the
rec/s changed for the same task (or sequence) between two
different runs, but it is not very useful to know how the rec/s
differs between Search and SearchTrav tasks. For
the latter, elapsedSec would bring more insight.

lucene

Parsing? Tokenization? Analysis!

Parsing

Tokenization

Core Analysis

Hints, Tips and Traps

Invoking the Analyzer

Indexing Analysis vs. Search Analysis

Implementing your own Analyzer

Field Section Boundaries

Token Position Increments

Table Of Contents

Benchmarking By Tasks

How to use

Benchmark "algorithm"

Supported tasks/commands

Benchmark properties

Example input algorithm and the result benchmark report

Results record counting clarified

Search Quality Benchmarking.

Document and Fieldable

Working with Documents

Table Of Contents

Search

Query Classes

TermQuery

BooleanQuery

Phrases

RangeQuery

PrefixQuery, WildcardQuery

FuzzyQuery

Changing Similarity

Changing Scoring — Expert Level

The Query Class

The Weight Interface

The Scorer Class

Why would I want to add my own Query?

Examples

Example Usage

New features 06/02/2005

New features 22/12/2004

Instructions