akurilov/mongoose
Deprecated. Use the "emcmongoose/mongoose" repo instead this.
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akurilov/mongoose repository overview
Mongoose
Mongoose
Description
Mongoose is a storage performance testing tool.
It is designed to be used for:
- Load Testing
- Stress Testing
- Soak/Longevity/Endurance Testing
- Volume Testing
- Smoke/Sanity Testing
Mongoose is able to sustain millions of concurrent connections and millions of operations per second.
Basically, Mongoose may be started very simply:
java -jar mongoose.jar
Features
Core Functionality
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Mongoose has rich configuration subsystem supporting the parametrization.
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Item is the unit of the load progress and metrics accounting. An item may be a file, cloud storage data object, some kind of token or something else. The data items describe the data to use to perform a load operation (some fixed or random size, content source, etc).
Mongoose may persist the items used in a run/job info to a specified output which later may be used as items input for another run/job.
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Different data may be used to perform a load on a storage. By default Mongoose uses random, uncompressible data. An user may supply custom data input file instead.
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In contradiction to all other comparable performance testing software, Mongoose doesn't use the threads to set up a specified concurrency level. This allows to use unlimited concurrency levels easily.
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The so called recycling feature allows to use the limited amount of items for an unlimited test run/load job by reusing these items again and again (circularly).
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A test step is an unit of a test run with its own configuration and metrics reporting.
An user is able to configure and identify a test step and limit it using one of available constraints.
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The metrics reported by Mongoose are designed to be most useful for performance analysis. The following metrics are accounted:
- Counts: items, bytes, elapsed and effective times.
- Rates: items per second and bytes per second.
- Timing distributions for operation durations and network latencies.
There are the metrics outputs performed periodically while a load job is running and total ones when a load job is finished.
Also, it's possible to obtain the highest-precision metrics (for each operation).
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The CRUD notation is used to describe the load operations. However, there are some extensions:
- Create may act as copying the items from the source to the destination
- Read may validate the data on the fly
- Read may be partial
- Update may act as append
- Noop operation is also available.
One of the most interesting things is a data reentrancy. This allows to validate the data read back from the storage successfully even after the data items have been randomly updated multiple times before.
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Mongoose is able to run the tests described with scenario files in the JSON format. Scenario syntax allow to:
- Inherit the jobs configuration
- Substitute the values using the environment variables.
- Execute the external commands
- Execute the steps sequentially or in parallel
- Execute the steps in a loop
- Execute the so called weighted load
- Execute the so called chain load
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Mongoose is able to work in the distributed mode what allows to scale out the load performed on a storage. In the distributed mode there's a controller which aggregates the results from the several remote storage drivers. Storage drivers perform the actual load on the storage.
Mongoose supports different storage types: a filesystem or a cloud storage. Currently, the following cloud storage APIs are supported:
It's possible to implement a custom storage driver to extend the storages support.
Other
Documentation
Tag summary
Content type
Image
Digest
Size
255.1 MB
Last updated
almost 10 years ago
docker pull akurilov/mongoose:travis-343