mirror of https://github.com/milvus-io/milvus.git
[skip ci]Update the content in markdowm format (#10070)
Signed-off-by: shiyu22 <shiyu.chen@zilliz.com>pull/10110/head
shiyu22
GitHub
parent
027103b4f6
commit
42d56575c8
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@ -22,22 +22,31 @@ The milvus_benchmark is a non-functional testing tool or service which allows us
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1. Set PYTHONPATH:
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`export PYTHONPATH=/your/project/path/milvus_benchmark`
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```bash
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$ export PYTHONPATH=/your/project/path/milvus_benchmark
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```
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2. Prepare data:
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if we need to use the sift/deep dataset as the raw data input, we need to mount NAS and update `RAW_DATA_DIR` in `config.py`, the example mount command:
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`sudo mount -t cifs -o username=test,vers=1.0 //172.16.70.249/test /test`
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```bash
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$ sudo mount -t cifs -o username=test,vers=1.0 //172.16.70.249/test /test
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```
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3. Install requirements:
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`pip install -r requirements.txt`
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```bash
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$ pip install -r requirements.txt
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```
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4. Install the [Python-SDK for milvus](https://github.com/milvus-io/pymilvus).
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5. Write test yaml and run with the yaml param:
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`cd milvus_benchmark/ && python main.py --local --host=* --port=19530 --suite=suites/2_insert_data.yaml`
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```bash
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$ cd milvus_benchmark/ && python main.py --local --host=* --port=19530 --suite=suites/2_insert_data.yaml
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```
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### Test suite
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@ -48,7 +57,7 @@ Test suite yaml defines the test process, users need to add test suite yaml if a
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#### Example
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Take the test file `2_insert_data.yaml` as an example
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```
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```yaml
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insert_performance:
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collections:
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-
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@ -76,7 +85,7 @@ insert_performance:
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While using argo workflow as benchmark pipeline, the test suite is made of both `client` and `server` configmap, an example will be like this:
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`server`
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```
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```yaml
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kind: ConfigMap
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apiVersion: v1
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metadata:
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@ -103,7 +112,7 @@ data:
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deploy_mode: "cluster"
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```
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`client`
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```
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```yaml
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kind: ConfigMap
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apiVersion: v1
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metadata:
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@ -144,9 +153,9 @@ There are several kinds of data types provided in benchmark:
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1. Insert from `local`: random generated vectors
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2. Insert from the file: the other data type such as `sift/deep`, the following list shows where the source data comes from, make sure to convert to `.npy` file format that can be loaded by `numpy`, and update the value of `RAW_DATA_DIR` in `config.py` to your own data path
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| data type | sift | deep |
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| ---- | ---- | ---- |
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| url | http://corpus-texmex.irisa.fr/ | https://github.com/erikbern/ann-benchmarks/
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| data type | sift | deep |
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| --------- | ------------------------------ | ------------------------------------------- |
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| url | http://corpus-texmex.irisa.fr/ | https://github.com/erikbern/ann-benchmarks/ |
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There are also many optional datasets could be used to test milvus, here is the reference: http://big-ann-benchmarks.com/index.html
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@ -159,7 +168,7 @@ Also, you should provide the field value of the source data file path `source_fi
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### Conponents
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- `main.py`
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The entry file: parse the input params and initialize the other conponent: `metric`, `env`, `runner`
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- `metric`
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@ -208,18 +217,18 @@ Also, you should provide the field value of the source data file path `source_fi
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As the above section mentioned, we will collect the test metrics after test case run finished, here is the main metric field:
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```
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run_id : each test suite will generate a run_id
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mode : run mode such as local
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server : describe server resource and server version
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hardware : server host
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env : server config
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status : run result
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err_message : error msg when run failed
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collection : collection info
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index : index type and index params
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search : search params
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run_params : extra run params
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metrics : metric type and metric value
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run_id : each test suite will generate a run_id
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mode : run mode such as local
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server : describe server resource and server version
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hardware : server host
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env : server config
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status : run result
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err_message : error msg when run failed
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collection : collection info
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index : index type and index params
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search : search params
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run_params : extra run params
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metrics : metric type and metric value
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```
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### How to visualize test result
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@ -229,7 +238,7 @@ As the metrics uploaded to the db (we use MongoDB currently), we suppose use Red
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For example, in order to find the most suitable insert batch size when preparing data with milvus, a benchmark test suite type named `bp_insert_performance` will run regularly, different `ni_per` in this suite yaml will be executed and the average response time and TPS (Number of rows inserted per second) will be collected.
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The query expression:
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```
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```json
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{
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"collection": "doc",
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"query": {
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