Ingestion troubleshooting FAQ

Realtime ingestion

The most common cause of this is because events being ingested are out of band of Druid’s windowPeriod. Druid realtime ingestion only accepts events within a configurable windowPeriod of the current time. You can verify this is what is happening by looking at the logs of your real-time process for log lines containing ingest/events/*. These metrics will indicate the events ingested, rejected, etc.

We recommend using batch ingestion methods for historical data in production.

Batch Ingestion

If you are trying to batch load historical data but no events are being loaded, make sure the interval of your ingestion spec actually encapsulates the interval of your data. Events outside this interval are dropped.

What types of data does Druid support?

Druid can ingest JSON, CSV, TSV and other delimited data out of the box. Druid supports single dimension values, or multiple dimension values (an array of strings). Druid supports long, float, and double numeric columns.

Not all of my events were ingested

Druid will reject events outside of a window period. The best way to see if events are being rejected is to check the Druid ingest metrics.

If the number of ingested events seem correct, make sure your query is correctly formed. If you included a count aggregator in your ingestion spec, you will need to query for the results of this aggregate with a longSum aggregator. Issuing a query with a count aggregator will count the number of Druid rows, which includes roll-up.

Where do my Druid segments end up after ingestion?

Depending on what druid.storage.type is set to, Druid will upload segments to some Deep Storage. Local disk is used as the default deep storage.

My stream ingest is not handing segments off

First, make sure there are no exceptions in the logs of the ingestion process. Also make sure that druid.storage.type is set to a deep storage that isn’t local if you are running a distributed cluster.

Other common reasons that hand-off fails are as follows:

1. Druid is unable to write to the metadata storage. Make sure your configurations are correct.

2. Historical processes are out of capacity and cannot download any more segments. You’ll see exceptions in the Coordinator logs if this occurs and the Coordinator console will show the Historicals are near capacity.

3. Segments are corrupt and cannot be downloaded. You’ll see exceptions in your Historical processes if this occurs.

4. Deep storage is improperly configured. Make sure that your segment actually exists in deep storage and that the Coordinator logs have no errors.

How do I get HDFS to work?

Make sure to include the druid-hdfs-storage and all the hadoop configuration, dependencies (that can be obtained by running command hadoop classpath on a machine where hadoop has been setup) in the classpath. And, provide necessary HDFS settings as described in deep storage .

How do I know when I can make query to Druid after submitting batch ingestion task?

You can verify if segments created by a recent ingestion task are loaded onto historicals and available for querying using the following workflow.

1. Submit your ingestion task.
2. Repeatedly poll the Overlord’s tasks API ( /druid/indexer/v1/task/{taskId}/status) until your task is shown to be successfully completed.
3. Poll the Segment Loading by Datasource API (/druid/coordinator/v1/datasources/{dataSourceName}/loadstatus) with forceMetadataRefresh=true and interval=<INTERVAL_OF_INGESTED_DATA> once. (Note: forceMetadataRefresh=true refreshes Coordinator’s metadata cache of all datasources. This can be a heavy operation in terms of the load on the metadata store but is necessary to make sure that we verify all the latest segments’ load status) If there are segments not yet loaded, continue to step 4, otherwise you can now query the data.
4. Repeatedly poll the Segment Loading by Datasource API (/druid/coordinator/v1/datasources/{dataSourceName}/loadstatus) with forceMetadataRefresh=false and interval=<INTERVAL_OF_INGESTED_DATA>. Continue polling until all segments are loaded. Once all segments are loaded you can now query the data. Note that this workflow only guarantees that the segments are available at the time of the Segment Loading by Datasource API call. Segments can still become missing because of historical process failures or any other reasons afterward.

I don’t see my Druid segments on my Historical processes

You can check the Coordinator console located at <COORDINATOR_IP>:<PORT>. Make sure that your segments have actually loaded on Historical processes. If your segments are not present, check the Coordinator logs for messages about capacity of replication errors. One reason that segments are not downloaded is because Historical processes have maxSizes that are too small, making them incapable of downloading more data. You can change that with (for example):

-Ddruid.segmentCache.locations=[{"path":"/tmp/druid/storageLocation","maxSize":"500000000000"}]

My queries are returning empty results

You can use a segment metadata query for the dimensions and metrics that have been created for your datasource. Make sure that the name of the aggregators you use in your query match one of these metrics. Also make sure that the query interval you specify match a valid time range where data exists.

How can I Reindex existing data in Druid with schema changes?

You can use DruidInputSource with the Parallel task to ingest existing druid segments using a new schema and change the name, dimensions, metrics, rollup, etc. of the segment. See DruidInputSource for more details. Or, if you use hadoop based ingestion, then you can use “dataSource” input spec to do reindexing.

See the Update existing data section of the data management page for more details.

How can I change the granularity of existing data in Druid?

In a lot of situations you may want to lower the granularity of older data. Example, any data older than 1 month has only hour level granularity but newer data has minute level granularity. This use case is same as re-indexing.

To do this use the DruidInputSource and run a Parallel task. The DruidInputSource will allow you to take in existing segments from Druid and aggregate them and feed them back into Druid. It will also allow you to filter the data in those segments while feeding it back in. This means if there are rows you want to delete, you can just filter them away during re-ingestion. Typically the above will be run as a batch job to say everyday feed in a chunk of data and aggregate it. Or, if you use hadoop based ingestion, then you can use “dataSource” input spec to do reindexing.

See the Update existing data section of the data management page for more details.

Real-time ingestion seems to be stuck

There are a few ways this can occur. Druid will throttle ingestion to prevent out of memory problems if the intermediate persists are taking too long or if hand-off is taking too long. If your process logs indicate certain columns are taking a very long time to build (for example, if your segment granularity is hourly, but creating a single column takes 30 minutes), you should re-evaluate your configuration or scale up your real-time ingestion.

More information

Getting data into Druid can definitely be difficult for first time users. Please don’t hesitate to ask questions in our IRC channel or on our google groups page.