QA/Sync/Test Plan/grinder tests: Difference between revisions
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== Technologies == | == Technologies == | ||
=== Testing Technologies === | |||
* Grinder | * Grinder | ||
** This will be the main test harness. We will write scripts against it using jython and use its built in tools in order to distribute the tests to multiple load generators | ** This will be the main test harness. We will write scripts against it using jython and use its built in tools in order to distribute the tests to multiple load generators | ||
Line 48: | Line 49: | ||
*** Heavy - it is built to handle many reads/writes per second. Hopefully we won't be load testing MongoDB. | *** Heavy - it is built to handle many reads/writes per second. Hopefully we won't be load testing MongoDB. | ||
*** REST api we can perform the same steps to communicate with sync as we can to communicate with the database | *** REST api we can perform the same steps to communicate with sync as we can to communicate with the database | ||
=== Important features of the sync system === | |||
* Load Balancer - Zeus | |||
** Will protect us from connection overrun | |||
** Questions about how it responds under high load condition and how firefox responds to that | |||
** Extremely scalable (will likely never be a bottle neck) | |||
* Back end application | |||
** Currently in PHP, will be migrated to python | |||
** Questions about through put of data | |||
* Database Server | |||
** Currently has a large cache (this means we need a lot of tests before we are truly in a production type environment) |
Revision as of 17:57, 25 May 2011
Overview
We would like to have a test framework that tests the sync servers for load and functionality. For this, we will use Grinder in order to mimic the action of many firefox clients simultaneously.
- We are trying to accomplish the following goals
- Create a continuous baseline testing framework (tied to Jenkins)
- Be able to answer questions about current and future growth.
- How can our app fail now?
- What types of services can we support in the future?
Test Cases
We are trying to answer the following questions
- How does the app fail under high load conditions? How does it fail?
- From a large data input?
- From a lot of connections?
- Which functions and potential use cases create a particularly high amount of load?
- High amounts of registration? (and corresponding initial sync?)
- Lots of empty requests? (as generated by instant sync?)
- Are any functions effected sooner by high load? (I.E. Are there any unexpected bottlenecks?)
- How do these services scale to
- More Features
- More Users
This translates roughly to the following test scripts
- Create Users
- Large Sync (initial sync)
- Small Sync (regular sync)
- Empty Sync (I'm checking in without data)
These test cases need to mimic the way the actions of firefox look to the sync server as closely as possible. Data is all encrypted so testing different types of data (bookmarks vs history) is irrelevant as far as the server is concerned.
Script use pattens
We currently have the ability to go through the logs and see how the sync server is being used. We can generate a profile based on this and do load testing that way. In addition, we can add scenarios that model other use cases. For example:
- A period with above normal registrations
- A period with high amounts of data payload
- Lots of small requests (what instant sync would give us)
The possibility to test different scenarios is limitless. One of the main uses for this down the road could be to test the potential impact of new services as they are developed.
Technologies
Testing Technologies
- Grinder
- This will be the main test harness. We will write scripts against it using jython and use its built in tools in order to distribute the tests to multiple load generators
- Advantages
- Distributed (We can use multiple machines to hit a server)
- Performant (No overhead of booting firefox)
- Built with HTTP based testing in mind (ideal for REST services including SYNC and beyond)
- MongoDB
- A database with a REST api will be used for data verification. A percentage of requests will get stored in a Mongo database in effort to make sure that our data is accurate at all loads
- Advantages
- Heavy - it is built to handle many reads/writes per second. Hopefully we won't be load testing MongoDB.
- REST api we can perform the same steps to communicate with sync as we can to communicate with the database
Important features of the sync system
- Load Balancer - Zeus
- Will protect us from connection overrun
- Questions about how it responds under high load condition and how firefox responds to that
- Extremely scalable (will likely never be a bottle neck)
- Back end application
- Currently in PHP, will be migrated to python
- Questions about through put of data
- Database Server
- Currently has a large cache (this means we need a lot of tests before we are truly in a production type environment)