iSharkFly-Docs
/
opensearch-docs-cn
mirror of https://github.com/iSharkFly-Docs/opensearch-docs-cn
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
opensearch-docs-cn/_benchmark/workloads/index.md

9.1 KiB
Raw Blame History

layout title nav_order has_children
default Workload reference 60 true

OpenSearch Benchmark workload reference

A workload is a specification of one or more benchmarking scenarios. A workload typically includes the following:

  • One or more data streams that are ingested into indices
  • A set of queries and operations that are invoked as part of the benchmark

Anatomy of a workload

The following example workload shows all of the essential elements needed to create a workload.json file. You can run this workload in your own benchmark configuration in order to understand how all of the elements work together:

{
  "description": "Tutorial benchmark for OpenSearch Benchmark",
  "indices": [
    {
      "name": "movies",
      "body": "index.json"
    }
  ],
  "corpora": [
    {
      "name": "movies",
      "documents": [
        {
          "source-file": "movies-documents.json",
          "document-count": 11658903, # Fetch document count from command line
          "uncompressed-bytes": 1544799789 # Fetch uncompressed bytes from command line
        }
      ]
    }
  ],
  "schedule": [
    {
      "operation": {
        "operation-type": "create-index"
      }
    },
    {
      "operation": {
        "operation-type": "cluster-health",
        "request-params": {
          "wait_for_status": "green"
        },
        "retry-until-success": true
      }
    },
    {
      "operation": {
        "operation-type": "bulk",
        "bulk-size": 5000
      },
      "warmup-time-period": 120,
      "clients": 8
    },
    {
      "operation": {
        "name": "query-match-all",
        "operation-type": "search",
        "body": {
          "query": {
            "match_all": {}
          }
        }
      },
      "iterations": 1000,
      "target-throughput": 100
    }
  ]
}

A workload usually consists of the following elements:

  • indices: Defines the relevant indices and index templates used for the workload.
  • corpora: Defines all document corpora used for the workload.
  • schedule: Defines operations and in what order the operations run in-line. Alternatively, you can use operations to group operations and the test_procedures parameter to specify the order of operations.
  • operations: Optional. Describes which operations are available for the workload and how they are parameterized.

Indices

To create an index, specify its name. To add definitions to your index, use the body option and point it to the JSON file containing the index definitions. For more information, see indices. For more information, see indices.

Corpora

The corpora element requires the name of the index containing the document corpus, for example, movies, and a list of parameters that define the document corpora. This list includes the following parameters:

  • source-file: The file name that contains the workload's corresponding documents. When using OpenSearch Benchmark locally, documents are contained in a JSON file. When providing a base_url, use a compressed file format: .zip, .bz2, .gz, .tar, .tar.gz, .tgz, or .tar.bz2. The compressed file must have one JSON file containing the name.
  • document-count: The number of documents in the source-file, which determines which client indices correlate to which parts of the document corpus. Each N client receives an Nth of the document corpus. When using a source that contains a document with a parent-child relationship, specify the number of parent documents.
  • uncompressed-bytes: The size, in bytes, of the source file after decompression, indicating how much disk space the decompressed source file needs.
  • compressed-bytes: The size, in bytes, of the source file before decompression. This can help you assess the amount of time needed for the cluster to ingest documents.

Operations

The operations element lists the OpenSearch API operations performed by the workload. For example, you can set an operation to create-index, which creates an index in the test cluster that OpenSearch Benchmark can write documents into. Operations are usually listed inside of schedule.

Schedule

The schedule element contains a list of actions and operations that are run by the workload. Operations run according to the order in which they appear in the schedule. The following example illustrates a schedule with multiple operations, each defined by its operation-type:

  "schedule": [
    {
      "operation": {
        "operation-type": "create-index"
      }
    },
    {
      "operation": {
        "operation-type": "cluster-health",
        "request-params": {
          "wait_for_status": "green"
        },
        "retry-until-success": true
      }
    },
    {
      "operation": {
        "operation-type": "bulk",
        "bulk-size": 5000
      },
      "warmup-time-period": 120,
      "clients": 8
    },
    {
      "operation": {
        "name": "query-match-all",
        "operation-type": "search",
        "body": {
          "query": {
            "match_all": {}
          }
        }
      },
      "iterations": 1000,
      "target-throughput": 100
    }
  ]
}

According to this schedule, the actions will run in the following order:

  1. The create-index operation creates an index. The index remains empty until the bulk operation adds documents with benchmarked data.
  2. The cluster-health operation assesses the health of the cluster before running the workload. In this example, the workload waits until the status of the cluster's health is green.
    • The bulk operation runs the bulk API to index 5000 documents simultaneously.
    • Before benchmarking, the workload waits until the specified warmup-time-period passes. In this example, the warmup period is 120 seconds.
  3. The clients field defines the number of clients that will run the remaining actions in the schedule concurrently.
  4. The search runs a match_all query to match all documents after they have been indexed by the bulk API using the 8 clients specified.
    • The iterations field indicates the number of times each client runs the search operation. The report generated by the benchmark automatically adjusts the percentile numbers based on this number. To generate a precise percentile, the benchmark needs to run at least 1,000 iterations.
    • Lastly, the target-throughput field defines the number of requests per second each client performs, which, when set, can help reduce the latency of the benchmark. For example, a target-throughput of 100 requests divided by 8 clients means that each client will issue 12 requests per second.

More workload examples

If you want to try certain workloads before creating your own, use the following examples.

Running unthrottled

In the following example, OpenSearch Benchmark runs an unthrottled bulk index operation for 1 hour against the movies index:

{
  "description": "Tutorial benchmark for OpenSearch Benchmark",
  "indices": [
    {
      "name": "movies",
      "body": "index.json"
    }
  ],
  "corpora": [
    {
      "name": "movies",
      "documents": [
        {
          "source-file": "movies-documents.json",
          "document-count": 11658903, # Fetch document count from command line
          "uncompressed-bytes": 1544799789 # Fetch uncompressed bytes from command line
        }
      ]
    }
  ],
  "schedule": [
  {
    "operation": "bulk",
    "warmup-time-period": 120,
    "time-period": 3600,
    "clients": 8
  }
]
}

Workload with a single task

The following workload runs a benchmark with a single task: a match_all query. Because no clients are indicated, only one client is used. According to the schedule, the workload runs the match_all query at 10 operations per second with 1 client, uses 100 iterations to warm up, and uses the next 100 iterations to measure the benchmark:

{
  "description": "Tutorial benchmark for OpenSearch Benchmark",
  "indices": [
    {
      "name": "movies",
      "body": "index.json"
    }
  ],
  "corpora": [
    {
      "name": "movies",
      "documents": [
        {
          "source-file": "movies-documents.json",
          "document-count": 11658903, # Fetch document count from command line
          "uncompressed-bytes": 1544799789 # Fetch uncompressed bytes from command line
        }
      ]
    }
  ],
{
  "schedule": [
    {
      "operation": {
        "operation-type": "search",
        "index": "_all",
        "body": {
          "query": {
            "match_all": {}
          }
        }
      },
      "warmup-iterations": 100,
      "iterations": 100,
      "target-throughput": 10
    }
  ]
}
}

Next steps