Oak Runnable Jar

This jar contains everything you need for a simple Oak installation.

The following runmodes are currently available:

* backup          : Backup an existing Oak repository.
* restore         : Restore a backup of an Oak repository.
* benchmark       : Run benchmark tests against different Oak repository fixtures.
* debug           : Print status information about an Oak repository.
* compact         : Segment compaction on a TarMK repository.
* upgrade         : Migrate existing Jackrabbit 2.x repository to Oak.
* server          : Run the Oak Server.
* console         : Start an interactive console.
* explore         : Starts a GUI browser based on java swing.
* graph           : Export the segment graph of a segment store to a file.
* history         : Trace the history of a node
* check           : Check the FileStore for inconsistencies
* scalability     : Run scalability tests against different Oak repository fixtures.
* recovery        : Run a _lastRev recovery on a MongoMK repository
* checkpoints     : Manage checkpoints
* tika            : Performs text extraction
* garbage         : Identifies blob garbage on a DocumentMK repository
* tarmkdiff       : Show changes between revisions on TarMk
* tarmkrecovery   : Lists candidates for head journal entries
* datastorecheck  : Consistency checker for data store 
* resetclusterid  : Resets the cluster id
* datastorecacheupgrade : Upgrades the JR2 DataStore cache
* help            : Print a list of available runmodes

Some of the features related to Jackrabbit 2.x are provided by oak-run-jr2 jar. See the Oak Runnable JR2 section for more details.

See the subsections below for more details on how to use these modes.

Backup

See the official documentation.

Restore

See the official documentation.

Debug

See the official documentation.

Console

The ‘console’ mode allows to work with an interactive console and browse an existing oak repository. Type ‘:help’ within the console to get a list of all supported commands. The console currently supports TarMK and MongoMK. To start the console for a TarMK repository, use:

$ java -jar oak-run-*.jar console /path/to/oak/repository

To start the console for a DocumentMK/Mongo repository, use:

$ java -jar oak-run-*.jar console mongodb://host

To start the console for a DocumentMK/RDB repository, use:

$ java -jar oak-run-*.jar --rdbjdbcuser username --rdbjdbcpasswd password console jdbc:...

To start the console connecting to a repository in read-write mode, use either of:

$ java -jar oak-run-*.jar console --read-write /path/to/oak/repository
$ java -jar oak-run-*.jar console --read-write mongodb://host
$ java -jar oak-run-*.jar console --read-write --rdbjdbcuser username --rdbjdbcpasswd password console jdbc:...

To specify FDS path while connecting to a repository, use --fds-path option (valid for segment and document repos):

$ java -jar oak-run-*.jar console --fds-path /path/to-data/store /path/to/oak/repository

Console is based on Groovy Shell and hence one can use all Groovy constructs. It also exposes the org.apache.jackrabbit.oak.console.ConsoleSession instance as through session variable. For example when using SegmentNodeStore you can dump the current segment info to a file

> new File("segment.txt") << session.workingNode.segment.toString()

In above case the workingNode captures the current NodeState which in case of Segment/TarMK is SegmentNodeState

You can also load external script at launch time via passing an extra argument as shown below

$ java -jar oak-run-*.jar console mongodb://host ":load /path/to/script.groovy"

Explore

The ‘explore’ mode starts a desktop browser GUI based on java swing which allows for read-only browsing of an existing oak repository.

$ java -jar oak-run-*.jar explore /path/to/oak/repository [skip-size-check]

Graph

The ‘graph’ mode export the segment graph of a file store to a text file in the Guess GDF format, which is easily imported into Gephi.

As the GDF format only supports integer values but the segment time stamps are encoded as long values an optional ‘epoch’ argument can be specified. If no epoch is given on the command line the start of the day of the last modified date of the ‘journal.log’ is used. The epoch specifies a negative offset translating all timestamps into a valid int range.

$ java -jar oak-run-*.jar graph [File] <options>

[File] -- Path to segment store (required)

Option           Description
------           -----------
--epoch <Long>   Epoch of the segment time stamps
                   (derived from journal.log if not
                   given)
--output <File>  Output file (default: segments.gdf)
--gc             Write the gc generation graph instead of the full graph
--pattern        Regular exception specifying which
                   nodes to include (optional). Ignore
                   when --gc is specified.

History

See the official documentation.

Check

See the official documentation.

Compact

See the official documentation.

Checkpoints

The ‘checkpoints’ mode can be used to list or remove repository checkpoints To start this mode, use:

$ java -jar oak-run-*.jar checkpoints { /path/to/oak/repository | mongodb://host:port/database } [list|rm-all|rm-unreferenced|rm <checkpoint>]

The ‘list’ option (treated as a default when nothing is specified) will list all existing checkpoints. The ‘rm-all’ option will wipe clean the ‘checkpoints’ node. The ‘rm-unreferenced’ option will remove all checkpoints except the one referenced from the async indexer (/:async@async). The ‘rm ’ option will remove a specific checkpoint from the repository.

Tika

The ‘tika’ mode enables performing text extraction, report generation and csv generation required for text extraction

Apache Jackrabbit Oak 1.4-SNAPSHOT
Non-option arguments:                                                         
tika [extract|report|generate]                                                
report   : Generates a summary report related to binary data                  
extract  : Performs the text extraction                                       
generate : Generates the csv data file based on configured NodeStore/BlobStore

Option                 Description                            
------                 -----------                            
-?, -h, --help         show help                              
--data-file <File>     Data file in csv format containing the 
                         binary metadata                      
--fds-path <File>      Path of directory used by FileDataStore
--nodestore            NodeStore detail                       
                         /path/to/oak/repository | mongodb:   
                         //host:port/database                 
--path                 Path in repository under which the     
                         binaries would be searched           
--pool-size <Integer>  Size of the thread pool used to        
                         perform text extraction. Defaults to 
                         number of cores on the system        
--store-path <File>    Path of directory used to store        
                         extracted text content               
--tika-config <File>   Tika config file path   

CSV File Format

Text extraction tool reads a csv file which contains details regarding those binary files from which text needs to be extracted. Entries in csv file look like below

43844ed22d640a114134e5a25550244e8836c00c#28705,28705,"application/octet-stream",,"/content/activities/jcr:content/folderThumbnail/jcr:content"
43844ed22d640a114134e5a25550244e8836c00c#28705,28705,"application/octet-stream",,"/content/snowboarding/jcr:content/folderThumbnail/jcr:content"
...

Where the columns are in following order

1. BlobId - Value of Jackrabbit ContentIdentity
2. Length
3. jcr:mimeType
4. jcr:encoding
5. path of parent node

The csv file can be generated programatically. For Oak based repositories it can be generated via generate command.

Generate

CSV file required for extract and report can be generated via generate mode

java -jar oak-run.jar tika \  
--fds-path /path/to/datastore \
--nodestore /path/to/segmentstore --data-file dump.csv generate

Above command would scan the NodeStore and create the csv file. This file can then be passed to extract command

Report

Tool can generate a summary report from a csv file

java -jar oak-run.jar tika \ 
    --data-file /path/to/binary-stats.csv report

The report provides a summary like

14:39:05.402 [main] INFO  o.a.j.o.p.tika.TextExtractorMain - MimeType Stats
        Total size         : 89.3 MB
        Total indexed size : 3.4 MB
        Total count        : 1048

               Type                 Indexed   Supported    Count       Size   
___________________________________________________________________________________
application/epub+zip              |      true|      true|  1       |    3.4 MB
image/png                         |     false|      true|  544     |   40.2 MB
image/jpeg                        |     false|      true|  444     |   34.0 MB
image/tiff                        |     false|      true|  11      |    6.1 MB
application/x-indesign            |     false|     false|  1       |    3.7 MB
application/octet-stream          |     false|     false|  39      |    1.2 MB
application/x-shockwave-flash     |     false|     false|  4       |  372.2 kB
application/pdf                   |     false|     false|  3       |  168.3 kB
video/quicktime                   |     false|     false|  1       |   95.9 kB

Extract

Extraction can be performed via following command

java -cp oak-run.jar:tika-app-1.8.jar \
org.apache.jackrabbit.oak.run.Main tika \
--data-file binary-stats.csv \
--store-path ./store 
--fds-path /path/to/datastore  extract

You would need to provide the tika-app jar which contains all the parsers. It can be downloaded from here. Extraction would then be performed in a multi threaded mode. Extracted text would be stored in the store-path

Upgrade

The ‘upgrade’ mode allows to migrate the contents of an existing Jackrabbit 2.x repository to Oak. To run the migration, use:

$ java -jar oak-run-*.jar upgrade [--datastore] \
      /path/to/jackrabbit/repository [/path/to/jackrabbit/repository.xml] \
      { /path/to/oak/repository | mongodb://host:port/database }

The source repository is opened from the given repository directory, and should not be concurrently accessed by any other client. Repository configuration is read from the specified configuration file, or from a repository.xml file within the repository directory if an explicit configuration file is not given.

The target repository is specified either as a local filesystem path to a directory (which will be automatically created if it doesn't already exist) of a new TarMK repository or as a MongoDB client URI that specifies the location of a MongoDB database where a new DocumentMK repository.

The --datastore option (if present) prevents the copying of binary data from a data store of the source repository to the target Oak repository. Instead the binaries are copied by reference, and you need to make the source data store available to the new Oak repository.

The content migration will automatically adjust things like node type, privilege and user account settings that work a bit differently in Oak. Unsupported features like same-name-siblings are migrated on a best-effort basis, with no strict guarantees of completeness. Warnings will be logged for any content inconsistencies that might be encountered; such content should be manually reviewed after the migration is complete. Note that things like search index configuration work differently in Oak than in Jackrabbit 2.x, and will need to be manually recreated after the migration. See the relevant documentation for more details.

Oak server mode

The Oak server mode starts a NodeStore or full Oak instance with the standard JCR plugins and makes it available over a simple HTTP mapping defined in the oak-http component. To start this mode, use:

$ java -jar oak-run-*.jar server [uri] [fixture] [options]

If no arguments are specified, the command starts an in-memory repository and makes it available at http://localhost:8080/. Specify an uri and a fixture argument to change the host name and port and specify a different repository backend.

The optional fixture argument allows to specify the repository implementation to be used. The following fixtures are currently supported:

Jackrabbit fixture requires Oak Runnable JR2 jar

Depending on the fixture the following options are available:

--cache 100            - cache size (in MB)
--host localhost       - MongoDB host
--port 27101           - MongoDB port
--db <name>            - MongoDB database (default is a generated name)
--clusterIds           - Cluster Ids for the Mongo setup: a comma separated list of integers
--base <file>          - Tar: Path to the base file
--mmap <64bit?>        - TarMK memory mapping (the default on 64 bit JVMs)
--rdbjdbcuri           - JDBC URL for RDB persistence
--rdbjdbcuser          - JDBC username (defaults to "")
--rdbjdbcpasswd        - JDBC password (defaults to "")
--rdbjdbctableprefix   - for RDB persistence: prefix for table names (defaults to "")

Examples:

$ java -jar oak-run-*.jar server
$ java -jar oak-run-*.jar server http://localhost:4503 Oak-Tar --base myOak
$ java -jar oak-run-*.jar server http://localhost:4502 Oak-Mongo --db myOak --clusterIds c1,c2,c3

See the documentation in the oak-http component for details about the available functionality.

Benchmark mode

The benchmark mode is used for executing various micro-benchmarks. It can be invoked like this:

$ java -jar oak-run-*.jar benchmark [options] [testcases] [fixtures]

The following benchmark options (with default values) are currently supported:

--host localhost       - MongoDB host
--port 27101           - MongoDB port
--db <name>            - MongoDB database (default is a generated name)
--mongouri             - MongoDB URI (takes precedence over host, port and db)
--dropDBAfterTest true - Whether to drop the MongoDB database after the test
--base target          - Path to the base file (Tar setup),
--mmap <64bit?>        - TarMK memory mapping (the default on 64 bit JVMs)
--cache 100            - cache size (in MB)
--wikipedia <file>     - Wikipedia dump
--runAsAdmin false     - Run test as admin session
--itemsToRead 1000     - Number of items to read
--report false         - Whether to output intermediate results
--csvFile <file>       - Optional csv file to report the benchmark results
--concurrency <levels> - Comma separated list of concurrency levels
--metrics false        - Enable metrics based stats collection
--rdbjdbcuri           - JDBC URL for RDB persistence (defaults to local file-based H2)
--rdbjdbcuser          - JDBC username (defaults to "")
--rdbjdbcpasswd        - JDBC password (defaults to "")
--rdbjdbctableprefix   - for RDB persistence: prefix for table names (defaults to "")

These options are passed to the test cases and repository fixtures that need them. For example the Wikipedia dump option is needed by the WikipediaImport test case and the MongoDB address information by the MongoMK and SegmentMK -based repository fixtures. The cache setting controls the bundle cache size in Jackrabbit, the NodeState cache size in MongoMK, and the segment cache size in SegmentMK.

The --concurrency levels can be specified as comma separated list of values, eg: --concurrency 1,4,8, which will execute the same test with the number of respective threads. Note that the beforeSuite() and afterSuite() are executed before and after the concurrency loop. eg. in the example above, the execution order is: beforeSuite(), 1x runTest(), 4x runTest(), 8x runTest(), afterSuite(). Tests that create their own background threads, should be executed with --concurrency 1 which is the default.

You can use extra JVM options like -Xmx settings to better control the benchmark environment. It‘s also possible to attach the JVM to a profiler to better understand benchmark results. For example, I’m using -agentlib:hprof=cpu=samples,depth=100 as a basic profiling tool, whose results can be processed with perl analyze-hprof.pl java.hprof.txt to produce a somewhat easier-to-read top-down and bottom-up summaries of how the execution time is distributed across the benchmarked codebase.

Some system properties are also used to control the benchmarks. For example:

-Dwarmup=5         - warmup time (in seconds)
-Druntime=60       - how long a single benchmark should run (in seconds)
-Dprofile=true     - to collect and print profiling data

The test case names like ReadPropertyTest, SmallFileReadTest and SmallFileWriteTest indicate the specific test case being run. You can specify one or more test cases in the benchmark command line, and oak-run will execute each benchmark in sequence. The benchmark code is located under org.apache.jackrabbit.oak.benchmark in the oak-run component. Each test case tries to exercise some tightly scoped aspect of the repository. You might remember many of these tests from the Jackrabbit benchmark reports like http://people.apache.org/~jukka/jackrabbit/report-2011-09-27/report.html that we used to produce earlier.

Finally the benchmark runner supports the following repository fixtures:

(Note that for Oak-RDB, the required JDBC drivers either need to be embedded into oak-run, or be specified separately in the class path. Furthermode, dropDBAfterTest is interpreted to drop the tables, not the database iself, if and only if they have been auto-created)

Once started, the benchmark runner will execute each listed test case against all the listed repository fixtures. After starting up the repository and preparing the test environment, the test case is first executed a few times to warm up caches before measurements are started. Then the test case is run repeatedly for one minute and the number of milliseconds used by each execution is recorded. Once done, the following statistics are computed and reported:

The most useful of these numbers is probably the 90% figure, as it shows the time under which the majority of test runs completed and thus what kind of performance could reasonably be expected in a normal usage scenario. However, the reason why all these different numbers are reported, instead of just the 90% one, is that often seeing the distribution of time across test runs can be helpful in identifying things like whether a bigger cache might help.

Finally, and most importantly, like in all benchmarking, the numbers produced by these tests should be taken with a large dose of salt. They DO NOT directly indicate the kind of application performance you could expect with (the current state of) Oak. Instead they are designed to isolate implementation-level bottlenecks and to help measure and profile the performance of specific, isolated features.

How to add a new benchmark

To add a new test case to this benchmark suite, you'll need to implement the Benchmark interface and add an instance of the new test to the allBenchmarks array in the BenchmarkRunner class in the org.apache.jackrabbit.oak.benchmark package.

The best way to implement the Benchmark interface is to extend the AbstractTest base class that takes care of most of the benchmarking details. The outline of such a benchmark is:

class MyTest extends AbstracTest {
    @Override
    protected void beforeSuite() throws Exception {
        // optional, run once before all the iterations,
        // not included in the performance measurements
    }
    @Override
    protected void beforeTest() throws Exception {
        // optional, run before runTest() on each iteration,
        // but not included in the performance measurements
    }
    @Override
    protected void runTest() throws Exception {
        // required, run repeatedly during the benchmark,
        // and the time of each iteration is measured.
        // The ideal execution time of this method is
        // from a few hundred to a few thousand milliseconds.
        // Use a loop if the operation you're hoping to measure
        // is faster than that.
    }
    @Override
    protected void afterTest() throws Exception {
        // optional, run after runTest() on each iteration,
        // but not included in the performance measurements
    }
    @Override
    protected void afterSuite() throws Exception {
        // optional, run once after all the iterations,
        // not included in the performance measurements
    }
}

The rough outline of how the benchmark will be run is:

test.beforeSuite();
for (...) {
    test.beforeTest();
    recordStartTime();
    test.runTest();
    recordEndTime();
    test.afterTest();
}
test.afterSuite();

You can use the loginWriter() and loginReader() methods to create admin and anonymous sessions. There's no need to logout those sessions (unless doing so is relevant to the benchmark) as they will automatically be closed after the benchmark is completed and the afterSuite() method has been called.

Similarly, you can use the addBackgroundJob(Runnable) method to add background tasks that will be run concurrently while the main benchmark is executing. The relevant background thread works like this:

while (running) {
    runnable.run();
    Thread.yield();
}

As you can see, the run() method of the background task gets invoked repeatedly. Such threads will automatically close once all test iterations are done, before the afterSuite() method is called.

Scalability mode

The scalability mode is used for executing various scalability suites to test the performance of various associated tests. It can be invoked like this:

$ java -jar oak-run-*.jar scalability [options] [suites] [fixtures]

The following scalability options (with default values) are currently supported:

--host localhost       - MongoDB host
--port 27101           - MongoDB port
--db <name>            - MongoDB database (default is a generated name)
--dropDBAfterTest true - Whether to drop the MongoDB database after the test
--base target          - Path to the base file (Tar setup),
--mmap <64bit?>        - TarMK memory mapping (the default on 64 bit JVMs)
--cache 100            - cache size (in MB)
--csvFile <file>       - Optional csv file to report the benchmark results
--rdbjdbcuri           - JDBC URL for RDB persistence (defaults to local file-based H2)
--rdbjdbcuser          - JDBC username (defaults to "")
--rdbjdbcpasswd        - JDBC password (defaults to "")

These options are passed to the various suites and repository fixtures that need them. For example the the MongoDB address information by the MongoMK and SegmentMK -based repository fixtures. The cache setting controls the NodeState cache size in MongoMK, and the segment cache size in SegmentMK.

You can use extra JVM options like -Xmx settings to better control the scalability suite test environment. It‘s also possible to attach the JVM to a profiler to better understand benchmark results. For example, I’m using -agentlib:hprof=cpu=samples,depth=100 as a basic profiling tool, whose results can be processed with perl analyze-hprof.pl java.hprof.txt to produce a somewhat easier-to-read top-down and bottom-up summaries of how the execution time is distributed across the benchmarked codebase.

The scalability suite creates the relevant repository load before starting the tests. Each test case tries to benchmark and profile a specific aspect of the repository.

Each scalability suite is configured to run a number of related tests which require the same base load to be available in the repository. Either the entire suite can be executed or individual tests within the suite can be run. If the suite names are specified like ScalabilityBlobSearchSuite then all the tests configured for the suite are executed. To execute particular tests in the suite, suite names appended with tests of the form suite:test1,test2 must be specified like ScalabilityBlobSearchSuite:FormatSearcher,NodeTypeSearcher. You can specify one or more suites in the scalability command line, and oak-run will execute each suite in sequence.

Finally the scalability runner supports the following repository fixtures:

(Note that for Oak-RDB, the required JDBC drivers either need to be embedded into oak-run, or be specified separately in the class path.)

Once started, the scalability runner will execute each listed suite against all the listed repository fixtures. After starting up the repository and preparing the test environment, the scalability suite executes all the configured tests to warm up caches before measurements are started. Then each configured test within the suite are run and the number of milliseconds used by each execution is recorded. Once done, the following statistics are computed and reported:

Also, for each test, the execution times are reported for each iteration/load configured.

The latter is more useful of these numbers as it shows how the individual execution times are scaling for each load.

How to add a new scalability suite

The scalability code is located under org.apache.jackrabbit.oak.scalabiity in the oak-run component.

To add a new scalability suite, you'll need to implement the ScalabilitySuite interface and add an instance of the new suite to the allSuites array in the ScalabilityRunner class, along with the test benchmarks, in the org.apache.jackrabbit.oak.scalability package. To implement the test benchmarks, it is required to extend the ScalabilityBenchmark abstract class and implement the execute() method. In addition, the methods beforeExecute() and afterExecute() can overridden to do processing before and after the benchmark executes.

The best way to implement the ScalabilitySuite interface is to extend the ScalabilityAbstractSuite base class that takes care of most of the benchmarking details. The outline of such a suite is:

class MyTestSuite extends ScalabilityAbstractSuite {
    @Override
    protected void beforeSuite() throws Exception {
        // optional, run once before all the iterations,
        // not included in the performance measurements
    }
    @Override
    protected void beforeIteration(ExecutionContext) throws Exception {
        // optional, Typically, this can be configured to create additional 
        // loads for each iteration.
        // This method will be called before each test iteration begins
    }

    @Override
    protected void executeBenchmark(ScalabilityBenchmark benchmark,
        ExecutionContext context) throws Exception {
        // required, executes the specified benchmark
    }
    
    @Override
    protected void afterIteration() throws Exception {
        // optional, executed after runIteration(),
        // but not included in the performance measurements
    }
    @Override
    protected void afterSuite() throws Exception {
        // optional, run once after all the iterations are complete,
        // not included in the performance measurements
    }
}

The rough outline of how the individual suite will be run is:

test.beforeSuite();
for (iteration...) {
    test.beforeIteration();
    for (benchmarks...) {
          recordStartTime();
          test.executeBenchmark();
          recordEndTime();
    }
    test.afterIteration();
}
test.afterSuite(); 

You can specify any context information to the test benchmarks using the ExecutionContext object passed as parameter to the beforeIteration() and the executeBenchmark() methods. ExecutionBenchmark exposes two methods getMap() and setMap() which can be used to pass context information.

You can use the loginWriter() and loginReader() methods to create admin and anonymous sessions. There's no need to logout those sessions (unless doing so is relevant to the test) as they will automatically be closed after the suite is complete and the afterSuite() method has been called.

Similarly, you can use the addBackgroundJob(Runnable) method to add background tasks that will be run concurrently while the test benchmark is executing. The relevant background thread works like this:

while (running) {
    runnable.run();
    Thread.yield();
}

As you can see, the run() method of the background task gets invoked repeatedly. Such threads will automatically close once all test iterations are done, before the afterSuite() method is called.

ScalabilityAbstractSuite defines some system properties which are used to control the suites extending from it :

-Dincrements=10,100,1000,1000     - defines the varying loads for each test iteration
-Dprofile=true                    - to collect and print profiling data
-Ddebug=true                      - to output any intermediate results during the suite 
                                    run

Recovery Mode

The recovery mode can be used to check the consistency of _lastRev fields of a MongoMK repository. It can be invoked like this:

$ java -jar oak-run-*.jar recovery [options] mongodb://host:port/database [dryRun]

The following recovery options (with default values) are currently supported:

--clusterId         - MongoMK clusterId (default: 0 -> automatic)

The recovery tool will only perform the check and fix for the given clusterId. It is therefore recommended to explicitly specify a clusterId. The tool will fix the documents it identified, unless the dryRun keyword is specified.

Garbage

The garbage mode can the used to identify blob garbage still referenced by documents in a MongoMK repository. It can be invoked like this:

$ java -jar oak-run-*.jar garbage [options] mongodb://host:port/database

The following recovery options (with default values) are currently supported:

--clusterId         - MongoMK clusterId (default: 0 -> automatic)

The tool will scan the store for documents with blob references and print a report with the top 100 documents with blob references considered garbage. The rank is based on the size of the referenced blobs.

Oak Runnable Jar - JR 2

This jar provides Jackrabbit 2.x related features

The following runmodes are currently available:

* upgrade     : Upgrade from Jackrabbit 2.x repository to Oak.
* benchmark   : Run benchmark tests against Jackrabbit 2.x repository fixture.
* server      : Run the JR2 Server.

Oak Mongo Shell Helpers

To simplify making sense of data created by Oak in Mongo a javascript file oak-mongo.js is provided. It includes some useful function to navigate the data in Mongo

$ wget https://s.apache.org/oak-mongo.js
$ mongo localhost/oak --shell oak-mongo.js
MongoDB shell version: 2.6.3
connecting to: localhost/oak
type "help" for help
> oak.countChildren('/oak:index/')
356787
> oak.getChildStats('/oak:index')
{ "count" : 356788, "size" : 127743372, "simple" : "121.83 MB" }
> oak.getChildStats('/')
{ "count" : 593191, "size" : 302005011, "simple" : "288.01 MB" }
>

For reporting any issue related to Oak the script provides a function to collect important stats and can be dumped to a file

$ mongo localhost/oak --eval "load('/path/to/oak-mongo.js');printjson(oak.systemStats());" --quiet > oak-stats.json

Oak TarMK Revision Diff

See the official documentation.

Oak TarMK Revision Recovery

Lists candidates for head journal entries. Uses a read-only store, so no updates will be performed on target repository.

$ java -jar oak-run-*.jar tarmkrecovery path/to/repository [--version-v10]

The following options are available:

--version-v10           - Uses V10 version repository reading (see OAK-2527)

Oak DataStore Check

Consistency checker for the DataStore. Also can be used to list all the blob references in the node store and all the blob ids available in the data store. Use the following command:

$ java -jar oak-run-*.jar datastorecheck [--id] [--ref] [--consistency] \
        [--store <path>|<mongo_uri>] \
        [--s3ds <s3ds_config>|--fds <fds_config>] \
        [--dump <path>] \
        [--track <DataStore local tracking path>]

The following options are available:

--id             - List all the ids in the data store
--ref            - List all the blob references in the node store
--consistency    - List all the missing blobs by doing a consistency check
Atleast one of the above should be specified

--store          - Path to the segment store of mongo uri (Required for --ref & --consistency option above)
--dump           - Path where to dump the files (Optional). Otherwise, files will be dumped in the user tmp directory.
--s3ds           - Path to the S3DataStore configuration file
--fds            - Path to the FileDataStore configuration file ('path' property is mandatory)
--track          - Path of the local reposity home folder (Optional). This will place a copy of the downloaded blob ids to be tracked.

Note: For using S3DataStore the following additional jars have to be downloaded - commons-logging-1.1.3.jar - aws-java-sdk-osgi-1.11.330.jar

The command to be executed for S3DataStore

java -classpath oak-run-*.jar:aws-java-sdk-osgi-1.11.330.jar:commons-logging-1.1.3.jar \
    org.apache.jackrabbit.oak.run.Main \
    datastorecheck --id --ref --consistency \
    --store <path>|<mongo_uri> \
    --s3ds <s3ds_config> \
    --dump <dump_path>

The config files should be formatted according to the OSGi configuration admin specification

E.g.
cat > org.apache.jackrabbit.oak.plugins.S3DataStore.config << EOF 
accessKey="XXXXXXXXX"
secretKey="YYYYYY"
s3Bucket="bucket1"
s3Region="region1"
EOF

cat > org.apache.jackrabbit.oak.plugins.FileDataStore.config << EOF 
path="/data/datastore"
EOF        

Reset Cluster Id

Resets the cluster id generated internally. Use the following command after stopping the server

$ java -jar oak-run-*.jar resetclusterid \
        { /path/to/oak/repository | mongodb://host:port/database }

The cluster id will be removed and will be generated on next server start up.

Oak DataStore Cache Upgrade

Upgrades the JR2 DataStore cache by moving files to the Upload staging and the download cache of the DataStore.

$ java -classpath oak-run-*.jar datastorecacheupgrade \
    --homeDir <home_directory> \
    --path <path> \
    --moveCache <true|false> \
    --deleteMapFile <true|false>

License

(see the top-level LICENSE.txt for full license details)

Collective work: Copyright 2012 The Apache Software Foundation.

Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

 http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.