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Use TiDB Lightning to Import Data in Parallel

Since v5.3.0, the physical import mode of TiDB Lightning supports the parallel import of a single table or multiple tables. By simultaneously running multiple TiDB Lightning instances, you can import data in parallel from different single tables or multiple tables. In this way, TiDB Lightning provides the ability to scale horizontally, which greatly reduces the time required to import large amount of data.

In technical implementation, TiDB Lightning records the meta data of each instance and the data of each imported table in the target TiDB, and coordinates the Row ID allocation range of different instances, the record of global Checksum, and the configuration changes and recovery of TiKV and PD.

You can use TiDB Lightning to import data in parallel in the following scenarios:

  • Import sharded schemas and sharded tables. In this scenario, multiple tables from multiple upstream database instances are imported into the downstream TiDB database by different TiDB Lightning instances in parallel.
  • Import single tables in parallel. In this scenario, single tables stored in a certain directory or cloud storage (such as Amazon S3) are imported into the downstream TiDB cluster by different TiDB Lightning instances in parallel. This is a new feature introduced in TiDB 5.3.0.

Considerations

No additional configuration is required for parallel import using TiDB Lightning. When TiDB Lightning is started, it registers meta data in the downstream TiDB cluster and automatically detects whether there are other instances migrating data to the target cluster at the same time. If there is, it automatically enters the parallel import mode.

But when migrating data in parallel, you need to take the following into consideration:

  • Handle conflicts between primary keys or unique indexes across multiple sharded tables
  • Optimize import performance

Handle conflicts between primary keys or unique indexes

When using the physical import mode to import data in parallel, ensure that there are no primary key or unique index conflicts between data sources, and between the tables in the target TiDB cluster, and there are no data writes in the target table during import. Otherwise, TiDB Lightning will fail to guarantee the correctness of the imported data, and the target table will contain inconsistent indexes after the import is completed.

Optimize import performance

Because TiDB Lightning needs to upload the generated Key-Value data to the TiKV node where each copy of the corresponding Region is located, the import speed is limited by the size of the target cluster. It is recommended to ensure that the number of TiKV instances in the target TiDB cluster and the number of TiDB Lightning instances are greater than n:1 (n is the number of copies of the Region). At the same time, the following requirements should be met to achieve optimal import performance:

  • Deploy each TiDB Lightning instance on a dedicated machine. Since one TiDB Lightning instance consumes all CPU resources by default, deploying multiple instances on a single machine cannot improve performance.
  • The total size of source files for each TiDB Lightning instance performing parallel import should be smaller than 5 TiB
  • The total number of TiDB Lightning instances should be smaller than 10.

When using TiDB Lightning to import shared databases and tables in parallel, choose an appropriate number of TiDB Lightning instances according to the amount of data.

  • If the MySQL data volume is less than 2 TiB, you can use one TiDB Lightning instance for parallel import.
  • If the MySQL data volume exceeds 2 TiB and the total number of MySQL instances is smaller than 10, it is recommended that you use one TiDB Lightning instance for each MySQL instance, and the number of parallel TiDB Lightning instances should not exceed 10.
  • If the MySQL data volume exceeds 2 TiB and the total number of MySQL instances exceeds 10, it is recommended that you allocate 5 to 10 TiDB Lightning instances for importing the data exported by these MySQL instances.

Next, this document uses two examples to detail the operation steps of parallel import in different scenarios:

  • Example 1: Use Dumpling + TiDB Lightning to import sharded databases and tables into TiDB in parallel
  • Example 2: Import single tables in parallel

Restrictions

TiDB Lightning exclusively uses some resources when it is running. If you need to deploy multiple TiDB Lightning instances on a single machine (which is not recommended for production environments), or on a disk shared by multiple machines, be aware of the following usage restrictions.

  • Set tikv-importer.sorted-kv-dir to a unique path for each TiDB Lightning instance. Multiple instances sharing the same path can lead to unintended behavior and may result in import failures or data errors.
  • Store each TiDB Lightning checkpoint separately. For more information about checkpoint configurations, see TiDB Lightning Checkpoints.
    • If you set checkpoint.driver = "file" (default), make sure that the path to the checkpoint is unique for each instance.
    • If you set checkpoint.driver = "mysql", you need to set a unique schema for each instance.
  • The log file for each TiDB Lightning should be set to a unique path. Sharing the same log file will impact log querying and troubleshooting.
  • If you use the Web Interface or Debug API, you need to set lightning.status-addr to a unique address for each instance; otherwise, the TiDB Lightning process fails to start due to port conflict.

Example 1: Use Dumpling + TiDB Lightning to Import Sharded Databases and Tables into TiDB in Parallel

In this example, assume that the upstream is a MySQL cluster with 10 sharded tables, with a total size of 10 TiB. You can Use 5 TiDB Lightning instances to perform parallel import, and each instance imports 2 TiB. It is estimated that the total import time (excluding the time required for Dumpling export) can be reduced from about 40 hours to about 10 hours.

Assume that the upstream library is named my_db, and the name of each sharded table is my_table_01 ~ my_table_10. You want to merge and import them into the downstream my_db.my_table table. The specific steps are described in the following sections.

Step 1: Use Dumpling to export data

Export two sharded tables on the 5 nodes where TiDB Lightning is deployed:

  • If the two sharded tables are in the same MySQL instance, you can use the --filter parameter of Dumpling to directly export them. When using TiDB Lightning to import, you can specify data-source-dir as the directory where Dumpling exports data to;
  • If the data of the two sharded tables are distributed on different MySQL nodes, you need to use Dumpling to separately export them. The exported data needs to be placed in the same parent directory but in different sub-directories. When using TiDB Lightning to perform parallel import, you need to specify data-source-dir as the parent directory.

For more information on how to use Dumpling to export data, see Dumpling.

Step 2: Configure TiDB Lightning data sources

Create a configuration file tidb-lightning.toml, and then add the following content:

[lightning]
status-addr = ":8289"

[mydumper]
# Specify the path for Dumpling to export data. If Dumpling performs several times and the data belongs to different directories, you can place all the exported data in the same parent directory and specify this parent directory here.
data-source-dir = "/path/to/source-dir"

[tikv-importer]
# Whether to allow importing data to tables with data. The default value is `false`.
# When you use parallel import mode, you must set it to `true`, because multiple TiDB Lightning instances are importing the same table at the same time.
incremental-import = true
# "local": The default mode. It applies to large dataset import, for example, greater than 1 TiB. However, during the import, downstream TiDB is not available to provide services.
# "tidb": You can use this mode for small dataset import, for example, smaller than 1 TiB. During the import, downstream TiDB is available to provide services.
backend = "local"

# Specify the path for local sorting data.
sorted-kv-dir = "/path/to/sorted-dir"

If the data source is stored in external storage such as Amazon S3 or GCS, you need to configure additional parameters for connection. You can specify parameters for such configuration. For example, the following example assumes that data is stored in Amazon S3:

./tidb-lightning --tidb-port=4000 --pd-urls=127.0.0.1:2379 --backend=local --sorted-kv-dir=/tmp/sorted-kvs \
    -d 's3://my-bucket/sql-backup'

For more parameter descriptions, see external storage URL.

Step 3: Start TiDB Lightning to import data

During parallel import, the server configuration requirements for each TiDB Lightning node are the same as the non-parallel import mode. Each TiDB Lightning node needs to consume the same resources. It is recommended to deploy them on different servers. For detailed deployment steps, see Deploy TiDB Lightning.

Start TiDB Lightning on each server in turn. If you use nohup to directly start it from the command line, it might exit due to the SIGHUP signal. So it is recommended to put nohup in the script, for example:

# !/bin/bash
nohup tiup tidb-lightning -config tidb-lightning.toml > nohup.out &

During parallel import, TiDB Lightning automatically performs the following checks after starting the task.

  • Check whether there is enough space on the local disk (controlled by the sort-kv-dir configuration) and on the TiKV cluster for importing data. To learn the required disk space, see Downstream storage space requirements and Resource requirements. TiDB Lightning samples the data sources and estimates the percentage of the index size from the sample result. Because indexes are included in the estimation, there might be cases where the size of the source data is less than the available space on the local disk, but still the check fails.
  • Check whether the regions in the TiKV cluster are distributed evenly and whether there are too many empty regions. If the number of empty regions exceeds max(1000, number of tables * 3), i.e. greater than the bigger one of "1000" or "3 times the number of tables ", then the import cannot be executed.
  • Check whether the data is imported in order from the data sources. The size of mydumper.batch-size is automatically adjusted based on the result of the check. Therefore, the mydumper.batch-size configuration is no longer available.

You can also turn off the check and perform a forced import with the lightning.check-requirements configuration. For more detailed checks, see TiDB Lightning prechecks

Step 4: Check the import progress

After starting the import, you can check the progress in either of the following ways:

  • Check the progress through the grep log keyword progress. It is updated every 5 minutes by default.
  • Check the progress through the monitoring console. For details, see TiDB Lightning Monitoring.

Wait for all TiDB Lightning instances to finish, then the entire import is completed.

Example 2: Import single tables in parallel

TiDB Lightning also supports parallel import of single tables. For example, import multiple single tables stored in Amazon S3 by different TiDB Lightning instances into the downstream TiDB cluster in parallel. This method can speed up the overall import speed. When remote storages such as Amazon S3 is used, the configuration parameters of TiDB Lightning are the same as those of BR. For more details, see external storage URL.

Assuming that the source files are stored in Amazon S3, the table files are my_db.my_table.00001.sql ~ my_db.my_table.10000.sql, a total of 10,000 SQL files. If you want to use 2 TiDB Lightning instances to speed up the import, you need to add the following settings in the configuration file:

[[mydumper.files]]
# the db schema file
pattern = '(?i)^(?:[^/]*/)*my_db-schema-create\.sql'
schema = "my_db"
type = "schema-schema"

[[mydumper.files]]
# the table schema file
pattern = '(?i)^(?:[^/]*/)*my_db\.my_table-schema\.sql'
schema = "my_db"
table = "my_table"
type = "table-schema"

[[mydumper.files]]
# Only import 00001~05000 and ignore other files
pattern = '(?i)^(?:[^/]*/)*my_db\.my_table\.(0[0-4][0-9][0-9][0-9]|05000)\.sql'
schema = "my_db"
table = "my_table"
type = "sql"

You can modify the configuration of the other instance to only import the 05001 ~ 10000 data files.

For other steps, see the relevant steps in Example 1.

Handle errors

Some TiDB Lightning nodes exit abnormally

If one or more TiDB Lightning nodes exit abnormally during a parallel import, identify the cause based on the logged error, and handle the error according to the error type:

  • If the error shows normal exit (for example, exit in response to a kill command) or termination by the operating system due to OOM, adjust the configuration and then restart the TiDB Lightning nodes.

  • If the error has no impact on data accuracy, for example, network timeout, run checkpoint-error-ignore by using tidb-lightning-ctl on all failed nodes to clean errors in the checkpoint source data. Then restart these nodes to continue importing data from checkpoints. For details, see checkpoint-error-ignore.

  • If the log reports errors resulting in data inaccuracy, for example, checksum mismatched, which indicates invalid data in the source file, run checkpoint-error-destroy by using tidb-lightning-ctl on all failed nodes to clean data imported to the failed tables as well as the checkpoint source data. For details, see checkpoint-error-destroy. This command removes the data imported to the failed tables downstream. Therefore, you need to re-configure and import the data of the failed tables on all TiDB Lightning nodes (including those that exit normally) by using the filters parameter.

During an import, an error "Target table is calculating checksum. Please wait until the checksum is finished and try again" is reported

Some parallel imports involve a large number of tables or tables with a small volume of data. In this case, it is possible that before one or more tasks start processing a table, other tasks of this table have finished and data checksum is in progress. At this time, an error Target table is calculating checksum. Please wait until the checksum is finished and try again is reported. In this case, you can wait for the completion of checksum and then restart the failed tasks. The error disappears and data accuracy is not affected.

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