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 * primary : Run a TarMK Cold Standby primary instance * standby : Run a TarMK Cold Standby standby instance * 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 * 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.
The ‘backup’ mode creates a backup from an existing oak repository. The most efficient way to backup the TarMK repository is to use a file system copy of the repository folder. The current backup implementation acts like a compaction to an enternal folder, on top of copying the state, it will also try to compress it, so it will significantly slower than what one might expect from a simple copy backup. Incremental backups (backup over an existing backup will still need to perform a full content diff) and will attempt to compact the diff. All optimisation flags used for offline compaction very much apply for this case as well. The FileStore backup doesn't need access to the DataStore, but if one is usually configured with the repository, it will need the following system property set to true in order to be able to perform the diffing -Doak.backup.UseFakeBlobStore=true. To start this mode, use:
$ java -jar oak-run-*.jar backup /path/to/oak/repository /path/to/backup
The ‘restore’ mode imports a backup of an existing oak repository. To start this mode, use:
$ java -jar oak-run-*.jar restore /path/to/oak/repository /path/to/backup
The ‘debug’ mode allows to obtain information about the status of the specified store. Currently this is only supported for the TarMK. To start this mode, use:
$ java -jar oak-run-*.jar debug /path/to/oak/repository [id...]
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:...
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"
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]
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.
Trace the history of a node backward through the revision history.
$ java -jar oak-run-*.jar history [File] <options>
[File] -- Path to segment store (required)
Option Description
------ -----------
--depth <Integer> Depth up to which to dump node states
(default: 0)
--journal journal file (default: journal.log)
--path Path for which to trace the history
(default: /)
The ‘check’ mode checks the storage of the FileStore for inconsistencies.
$ java -jar oak-run-*.jar check <options>
--bin [Long] read the n first bytes from binary
properties. -1 for all bytes.
(default: 0)
--deep [Long] enable deep consistency checking. An
optional long specifies the number
of seconds between progress
notifications (default:
9223372036854775807)
--journal journal file (default: journal.log)
--path path to the segment store (required)
For example
$ java -jar oak-run-*.jar check -p repository/segmentstore -d
Checks the files in the repository/segmentstore directory for inconsistencies. It will start with the latest revision in the journal.log file going back revision by revision until a full traversal succeeds. During the traversal the current path will is output to the console every 1 second. When done the latest good revision is output.
Searching for last good revision in journal.log Checking revision b82167c3-1ceb-4404-a67f-9c542e854086:240872 Traversing / Error while traversing /home/users/foo: Segment 476e1abd-0ea0-44a8-ac3c-3a3bd Traversed 50048 nodes and 303846 properties Broken revision b82167c3-1ceb-4404-a67f-9c542e854086:240872 Checking revision 84d693cb-f214-4d19-a1cd-8b5766d50fdb:250028 Checking /home/users/foo Traversed 11612889 nodes and 27511640 properties Found latest good revision 84d693cb-f214-4d19-a1cd-8b5766d50fdb:250028 Searched through 2 of 390523 revisions
The ‘primary’ mode starts a TarMK Cold Standby primary instance (master) listening on a TCP/IP port for connecting slaves.
$ java -jar oak-run-*.jar primary [options] /path/to/TarMK
The following options are available:
--port 8023 - port to listen at --admissible 127.0.0.1 - admissible client IP range or host name --secure - use secure connections
The ‘standby’ mode starts a TarMK Cold Standby standby instance (slave) to create or update a continuous backup from a Cold Standby primary.
$ java -jar oak-run-*.jar standby [options] /path/to/TarMK
The following options are available:
--port 8023 - port to connect to --host 127.0.0.1 - host to connect to --secure - use secure connections --interval 5 - schedule the slave to run continuously, connecting every n seconds
The ‘compact’ mode runs the segment compaction operation on the provided TarMK repository. To start this mode, use:
$ java -jar oak-run-*.jar compact [path] <options>
[File] -- Path to segment store (required)
Option Description
------ -----------
--force Force compaction and ignore non
matching segment version
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.
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
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
The csv file can be generated programatically. For Oak based repositories it can be generated via generate command.
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
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
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
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.
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:
| Fixture | Description |
|---|---|
| Jackrabbit(*) | Jackrabbit with the default embedded Derby bundle PM |
| Oak-Memory | Oak with default in-memory storage |
| Oak-MemoryNS | Oak with default in-memory NodeStore |
| Oak-Mongo | Oak with the default Mongo backend |
| Oak-Mongo-FDS | Oak with the default Mongo backend and FileDataStore |
| Oak-MongoNS | Oak with the Mongo NodeStore |
| Oak-Tar | Oak with the Tar backend (aka Segment NodeStore) |
| Oak-Tar-FDS | Oak with the Tar backend and FileDataStore |
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.
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 --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:
| Fixture | Description |
|---|---|
| Jackrabbit | Jackrabbit with the default embedded Derby bundle PM |
| Oak-Memory | Oak with default in-memory storage |
| Oak-MemoryNS | Oak with default in-memory NodeStore |
| Oak-Mongo | Oak with the default Mongo backend |
| Oak-Mongo-FDS | Oak with the default Mongo backend and FileDataStore |
| Oak-MongoNS | Oak with the Mongo NodeStore |
| Oak-Tar | Oak with the Tar backend (aka Segment NodeStore) |
| Oak-RDB | Oak with the DocumentMK/RDB persistence |
(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:
| Column | Description |
|---|---|
| C | concurrency level |
| min | minimum time (in ms) taken by a test run |
| 10% | time (in ms) in which the fastest 10% of test runs |
| 50% | time (in ms) taken by the median test run |
| 90% | time (in ms) in which the fastest 90% of test runs |
| max | maximum time (in ms) taken by a test run |
| N | total number of test runs in one minute (or more) |
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.
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.
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:
| Fixture | Description |
|---|---|
| Oak-Memory | Oak with default in-memory storage |
| Oak-MemoryNS | Oak with default in-memory NodeStore |
| Oak-Mongo | Oak with the default Mongo backend |
| Oak-Mongo-FDS | Oak with the default Mongo backend and FileDataStore |
| Oak-MongoNS | Oak with the Mongo NodeStore |
| Oak-Tar | Oak with the Tar backend (aka Segment NodeStore) |
| Oak-RDB | Oak with the DocumentMK/RDB persistence |
(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:
| Column | Description |
|---|---|
| min | minimum time (in ms) taken by a test run |
| 10% | time (in ms) in which the fastest 10% of test runs |
| 50% | time (in ms) taken by the median test run |
| 90% | time (in ms) in which the fastest 90% of test runs |
| max | maximum time (in ms) taken by a test run |
| N | total number of test runs in one minute (or more) |
Also, for each test, the execution times are reported for each iteration/load configured.
| Column | Description |
|---|---|
| Load | time (in ms) taken by a test run |
The latter is more useful of these numbers as it shows how the individual execution times are scaling for each load.
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
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.
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.
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.
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
Show changes between revisions on TarMk. It uses a read-only store, so it can also be used on a running system without the need to shut down.
$ java -jar oak-run-*.jar tarmkdiff path/to/repository [--list] [--diff=R0..R1] [--incremental] [--ignore-snfes] [--output=/path/to/output/file]
The following options are available:
--list - Lists the existing revisions. will ignore other params if this is provided --diff - Revision diff interval. Ex '--diff=R0..R1'. 'HEAD' can be used to reference the latest head revision, ie. '--diff=R0..HEAD' --incremental - Runs diffs between each subsequent revisions in the provided interval (false by default) --ignore-snfes - Ignores SegmentNotFoundExceptions and continues running the diff (experimental) (false by default) --path - Filter diff by given path --output - Output file name (generated randomly if not provided)
Output sample
rev 7583946d-1817-4716-a05c-660ee52ddce0.ff94..c238cd7d-87a0-4cca-aa14-80b75e8ab81d.fb3e
^ /oak:index
^ /oak:index/lucene
^ /oak:index/lucene/:data
- /oak:index/lucene/:data/_3729.cfs
+ /oak:index/lucene/:data/_372d.si
+ blobSize<LONG> = 1047552
+ jcr:lastModified<LONG> = 1447948037017
+ jcr:data<BINARIES>[1] = [252 bytes]
- /oak:index/lucene/:data/segments_37bv
+ /oak:index/lucene/:data/_372d.cfe
+ blobSize<LONG> = 1047552
+ jcr:lastModified<LONG> = 1447948037017
+ jcr:data<BINARIES>[1] = [224 bytes]
- /oak:index/lucene/:data/_3729.si
- /oak:index/lucene/:data/_3729.cfe
+ /oak:index/lucene/:data/_372d.cfs
+ blobSize<LONG> = 1047552
+ jcr:lastModified<LONG> = 1447948037017
+ jcr:data<BINARIES>[1] = [907 bytes]
+ /oak:index/lucene/:data/segments_37bz
+ blobSize<LONG> = 1047552
+ jcr:lastModified<LONG> = 1447948045167
+ jcr:data<BINARIES>[1] = [863 bytes]
^ /oak:index/lucene/:data/segments.gen
^ jcr:lastModified
- jcr:lastModified<LONG> = 1447947918027
+ jcr:lastModified<LONG> = 1447948045167
^ jcr:data
- jcr:data<BINARIES>[1] = [20 bytes]
+ jcr:data<BINARIES>[1] = [20 bytes]
^ /oak:index/lucene/:status
^ lastUpdated
- lastUpdated<DATE> = 2015-11-19T10:45:18.027-05:00
+ lastUpdated<DATE> = 2015-11-19T10:47:25.167-05:00
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)
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>]
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 - Lists all the missing blobs by doind 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)
Note: For using S3DataStore the following additional jars have to be downloaded - commons-logging-1.1.3.jar - httpcore-4.4.4.jar - aws-java-sdk-osgi-1.10.76.jar
The command to be executed for S3DataStore
java -classpath oak-run-*.jar:httpcore-4.4.4.jar:aws-java-sdk-osgi-1.10.76.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
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.
(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.