Table of Contents

insert-segment-to-db Tool

insert-segment-to-db is a tool that can insert segments into Druid metadata storage. It is intended to be used to update the segment table in metadata storage after people manually migrate segments from one place to another. It can also be used to insert missing segments into Druid, or even recover metadata storage by telling it where the segments are stored.

Note: This tool simply scans the deep storage directory to reconstruct the metadata entries used to locate and identify each segment. It does not have any understanding about whether those segments should actually be written to the metadata storage. In certain cases, this can lead to undesired or inconsistent results. Some examples of things to watch out for: - Dropped datasources will be re-enabled. - The latest version of each segment set will be loaded by Druid, which in some cases may not be the version you actually want. An example of this is a bad compaction job that generates segments which need to be manually rolled back by removing that version from the metadata table. If these segments are not also removed from deep storage, they will be imported back into the metadata table and overshadow the correct version. - Some indexers such as the Kafka indexing service have the potential to generate more than one set of segments that have the same segment ID but different contents. When the metadata is first written, the correct set of segments is referenced and the other set is normally deleted from deep storage. It is possible however that an unhandled exception could result in multiple sets of segments with the same segment ID remaining in deep storage. Since this tool does not know which one is the 'correct' one to use, it will simply select the newest segment set and ignore the other versions. If the wrong segment set is picked, the exactly-once semantics of the Kafka indexing service will no longer hold true and you may get duplicated or dropped events.

With these considerations in mind, it is recommended that data migrations be done by exporting the original metadata storage directly, since that is the definitive cluster state. This tool should be used as a last resort when a direct export is not possible.

Note: This tool expects users to have Druid cluster running in a "safe" mode, where there are no active tasks to interfere with the segments being inserted. Users can optionally bring down the cluster to make 100% sure nothing is interfering.

In order to make it work, user will have to provide metadata storage credentials and deep storage type through Java JVM argument or file. Specifically, this tool needs to know:

Besides the properties above, you also need to specify the location where the segments are stored and whether you want to update descriptor.json (partitionNum_descriptor.json for HDFS data storage). These two can be provided through command line arguments.

--workingDir (Required)

The directory URI where segments are stored. This tool will recursively look for segments underneath this directory
and insert/update these segments in metdata storage.
Attention: workingDir must be a complete URI, which means it must be prefixed with scheme type. For example,

--updateDescriptor (Optional)

if set to true, this tool will update `loadSpec` field in `descriptor.json` (`partitionNum_descriptor.json` for HDFS data storage) if the path in `loadSpec` is different from
where `desciptor.json` (`partitionNum_descriptor.json` for HDFS data storage) was found. Default value is `true`.

Note: you will also need to load different Druid extensions per the metadata and deep storage you use. For example, if you use mysql as metadata storage and HDFS as deep storage, you should load mysql-metadata-storage and druid-hdfs-storage extensions.


Suppose your metadata storage is mysql and you've migrated some segments to a directory in HDFS, and that directory looks like this,

Directory path: /druid/storage/wikipedia

├── 2013-08-31T000000.000Z_2013-09-01T000000.000Z
│   └── 2015-10-21T22_07_57.074Z
│           ├── 0_descriptor.json
│           └──
├── 2013-09-01T000000.000Z_2013-09-02T000000.000Z
│   └── 2015-10-21T22_07_57.074Z
│           ├── 0_descriptor.json
│           └──
├── 2013-09-02T000000.000Z_2013-09-03T000000.000Z
│   └── 2015-10-21T22_07_57.074Z
│           ├── 0_descriptor.json
│           └──
└── 2013-09-03T000000.000Z_2013-09-04T000000.000Z
    └── 2015-10-21T22_07_57.074Z
            ├── 0_descriptor.json

To load all these segments into mysql, you can fire the command below,

org.apache.druid.cli.Main tools insert-segment-to-db --workingDir hdfs://host:port//druid/storage/wikipedia --updateDescriptor true

In this example, mysql and deep storage type are provided through Java JVM arguments, you can optionally put all of them in a runtime.properites file and include it in the Druid classpath. Note that we also include mysql-metadata-storage and druid-hdfs-storage in the extension list.

After running this command, the segments table in mysql should store the new location for each segment we just inserted. Note that for segments stored in HDFS, druid config must contain core-site.xml as described in Druid Docs, as this new location is stored with relative path.

It is also possible to use s3 as deep storage. In order to work with it, specify s3 as deep storage type and load druid-s3-extensions as an extension.

org.apache.druid.cli.Main tools insert-segment-to-db --workingDir "druid/storage/wikipedia" --updateDescriptor true

Note that you can provide the location of segments with either or --workingDir. If both are specified, --workingDir gets higher priority. is to determine the length of a partial list in requesting a object listing to s3, which defaults to 1000.