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Mongoose documentation is located in the project root under the doc directory since v4.
Use README.md
as new documentation entry. The wiki content will be deprecated and will remain suiteble for v3.x.x
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Mongoose is a storage performance testing tool.
It is designed to be used for:
Mongoose is able to sustain millions of concurrent connections and millions of operations per second.
Please refer to the deployment page for the details.
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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 using the specific DSL. Scenario syntax allow to:
- Configure the load steps
- Organize the steps sequentially or in parallel
- Reuse the step configuration
- Substitute the values using environment variables
- Execute the external commands
- Execute advanced load steps, such as weighted load either 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:
It's possible to implement a custom storage driver to extend the storages support.
- Gennady Eremeev
- Ilya Kisliakovsky
- Kirill Gusakov
- Mikhail Danilov
- Mikhail Malygin
- Olga Zhavzharova