Lindorm:Migrate full data from TSDB to LindormTSDB

Prerequisites

• Linux or macOS is installed on the client and the following environment is installed:

  • Java Development Kit (JDK) 1.8 or later is installed.

  • Python 2.x or 3.x is installed.

• The version of the TSDB instance is 2.7.4 or later.

• A Lindorm instance is created and LindormTSDB is activated for the instance. For more information, see Create an instance.

Background information

LindormTSDB is developed by Alibaba Cloud and is compatible with most APIs of TSDB. Compared with TSDB, LindormTSDB offers higher performance with lower costs and supports more features. TSDB is no longer available for sale. We recommend that you migrate all data in your TSDB instances to LindormTSDB.

Process

Usage notes

• If your application is deployed on an ECS instance, we recommend that you deploy the ECS instance and the Lindorm instance in the same VPC as the TSDB instance that you want to migrate to ensure the communication between the instances.

• If you migrate data from a TSDB instance to LindormTSDB over the Internet, make sure that the public endpoints of the Lindorm and TSDB instances are enabled, and the IP address of your client is added to the whitelists of the Lindorm and TSDB instances. For more information, see Configure whitelists.

• During the migration process, data is read from the TSDB instance and is written to LindormTSDB. Therefore, check whether your business is affected during the migration from the following dimensions before you migrate data:

  • The specification of the TSDB instance

  • The specifications of the environment (such as an ECS instance) on which applications are deployed

  • The number of time series in the TSDB instance

  • The total size of data that you want to migrate

  • The average frequency at which data in each time series are reported

  • The time range of the data that you want to migrate

  • The interval at which each migration task is split

  Note

  For more information about performance evaluation, see Performance testing.

• Data written by using the multi-value data model cannot be queried by using SQL statements. To use SQL statements to query the migrated data, create a time series table before you migrate data to LindormTSDB.

• By default, the timestamps used in LindormTSDB are 13 bits in length, which indicate time values in milliseconds. The timestamps used in TSDB are 10 bits in length, which indicate time values in seconds. After data is migrated from the TSDB instance to LindormTSDB, the timestamps of the data are converted to 13 bits in length.

• The single-value model is not recommended in LindormTSDB for writing data. Therefore, data that has been written to the TSDB instance by using the single-value model must be queried by using the multi-value data model. The following sample code shows how to query data that has been written to the TSDB instance by using the single-value data model in TSDB and LindormTSDB:

  // // The statement used to query data in TSDB.
  curl -u username:password ts-xxxxx:3242/api/query -XPOST -d '{
      "start": 1657004460,
      "queries": [
          {
              "aggregator": "none",
              "metric": "test_metric"
          }
      ]
  }'
  // The query results in TSDB.
  [
      {
          "aggregateTags": [],
          "dps": {
              "1657004460": 1.0
          },
          "fieldName": "",
          "metric": "test_metric",
          "tags": {
              "tagkey1": "1"
          }
      }
  ]
  
  // The statement used to query data in LindormTSDB.
  curl -u username:password ld-xxxxx:8242/api/mquery -XPOST -d '{
      "start":1657004460,
      "queries": [
          {
              "metric": "test_metric",
              "fields": [
                  {
                      "field": "*",
                      "aggregator": "none"
                  }
              ],
              "aggregator": "none"
          }
      ]
  }'
  // The query results in LindormTSDB.
  [
    {
      "aggregatedTags": [],
      "columns": [
        "timestamp",
        "value"
      ],
      "metric": "test_metric",
      "tags": {
        "tagkey1": "1"
      },
      "values": [
        [
          1657004460000,
          1.0
        ]
      ]
    }
  ]

Configure a data migration task

{
    "job": {
        "setting": {
            "speed": {
                "channel": 1
            },
            "errorLimit": {
                "record": 0,
                "percentage": 0.00
            }
        },
        "content": [
            {
                "reader": {
                    "name": "tsdbreader",
                    "parameter": {
                        "sinkDbType": "LINDORM-MIGRATION",
                        "endpoint": "ts-xxxx:3242",
                        "beginDateTime": "2022-5-2 00:00:00",
                        "endDateTime": "2022-7-2 00:00:00",
                        "splitIntervalMs": 86400000,
                        "jobName":"myjob",
                        "selfId":1,
                        "jobIds":[1],
                        "oidPath":"{$myworkplace}/oidfile",
                        "oidBatch":100,
                        "oidCache":true
                    }
                },
                "writer": {
                    "name": "tsdbwriter",
                    "parameter": {
                        "endpoint": "ld-xxxx:8242",
                        "multiField":true,
                        "batchSize":500
                    }
                }
            }
        ]
    }
}
                

Start the data migration task

1. Download the migration tool for time series data.

2. Run the following command to decompress the downloaded package of the migration tool:

   tar -zxvf tsdb2lindorm.tar.gz

3. Run the following command to start the data migration task:

   python datax/bin/datax.py  --jvm="-Xms8G -Xmx8G" job.json > job.result

   Note

   Replace job in the preceding command with the name of the configuration file for the actual migration task.

   After the command is run, check whether error information is recorded in the job.result file. If no error information is returned, the migration task is successful.

4. (Optional)If the migration task fails, you can execute the following multi-value statement to query the task status list of the TSDB instance:

   curl -u username:password ts-****:3242/api/mquery -XPOST -d '{
       "start": 1,
       "queries": [
           {
               "metric": "internal_datax_jobjobName",
               "fields": [
                   {
                       "field": "*",
                       "aggregator": "none"
                   }
               ]
           }
       ]
   }'

   Note

   • username:password: Replace this value with the account and password that you use to access the TSDB instance. For more information, see Manage accounts.

   • ts-****: Replace this value with the ID of the TSDB instance.

   • jobName: Replace this value with the name of the actual migration task. Example: internal_datax_jobmyjob.

   The following table describes the returned task status list.

   Timestamp (endtime)	jobId (Tag)	state(field)
   1651795199999 (2022-05-05 23:59:59.999)	3	ok
   1651795199999 (2022-05-05 23:59:59.999)	2	ok
   1651795199999 (2022-05-05 23:59:59.999)	1	ok
   1651881599999 (2022-05-06 23:59:59.999)	2	ok

   To prevent an executed migration task from being executed again, modify the value of beginDateTime in the job.json file before you start the task. In this example, the value of beginDateTime is changed to 2022-05-06 00:00:00.

Performance test

Before you migrate data from a TSDB instance, you must evaluate the performance of the TSDB instance. The following tables show the performance test results of TSDB Basic Edition instance and TSDB Standard Edition instances for reference.

• Test results of two TSDB Basic Edition II instances each with 4 CPU cores and 8 GB of memory

  Tests	Amount of data	Number of processes in a task	Configurations	Size of time series files	Number of data points migrated per second	Migration duration	TSDB resource utilization
  1	Total number of time series: 30,000 Total number of data points: 86,400,000	1	channel:2 oidCache:true oidBatch:100 splitInterval:6h mem:-Xms6G -Xmx6G	1.5 MB	230000	12 minutes 30 seconds	CPU utilization: 30%
  2	Total number of time series: 6,000,000 Total number of data points: 2,592,000,000	1	channel:10 oidCache:true oidBatch:100 splitInterval:6h mem:-Xms8G -Xmx8G	292 MB	200000	2 hours 55 minutes 30 seconds	CPU utilization: 70% to 90%
  3	Total number of time series: 30,000,000 Total number of data points: 4,320,000,000	1	channel:10 oidCache:false oidBatch:100 splitInterval:6h mem:-Xms28G -Xmx28G	1.5 GB	140000	9 hours	CPU utilization: 40% to 80%
  4	Total number of time series: 30,000,000 Total number of data points: 4,320,000,000	3	channel:10 oidCache:false oidBatch:100 splitInterval:6h mem:-Xms8G -Xmx8G	1.5 GB	250000	5 hours	CPU utilization: 90%

• Test results of two TSDB Standard Edition I instances each with 8 CPU cores and 16 GB of memory

  Amount of data	Number of processes in a task	Configurations	Size of time series files	Number of data points migrated per second	Migration duration	TSDB resource utilization
  Total number of time series: 40,000,000 Total number of data points: 5,760,000,000	3	channel:10 oidCache:false oidBatch:100 splitInterval:6h mem:-Xms8G -Xmx8G	2 GB	150000~200000	9 hours	CPU utilization: 10% to 20%