A report containing information about the number of rows ingested, and any parse exceptions that occurred is available for both completed tasks and running tasks.
The reporting feature is supported by the [simple native batch task](../ingestion/native-batch.md#simple-task), the Hadoop batch task, and Kafka and Kinesis ingestion tasks.
When a task is running, a live report containing ingestion state, unparseable events and moving average for number of events processed for 1 min, 5 min, 15 min time window can be retrieved at:
The `ingestionStatsAndErrors` report provides information about row counts and errors.
The `ingestionState` shows what step of ingestion the task reached. Possible states include:
*`NOT_STARTED`: The task has not begun reading any rows
*`DETERMINE_PARTITIONS`: The task is processing rows to determine partitioning
*`BUILD_SEGMENTS`: The task is processing rows to construct segments
*`COMPLETED`: The task has finished its work.
Only batch tasks have the DETERMINE_PARTITIONS phase. Realtime tasks such as those created by the Kafka Indexing Service do not have a DETERMINE_PARTITIONS phase.
`unparseableEvents` contains lists of exception messages that were caused by unparseable inputs. This can help with identifying problematic input rows. There will be one list each for the DETERMINE_PARTITIONS and BUILD_SEGMENTS phases. Note that the Hadoop batch task does not support saving of unparseable events.
the `rowStats` map contains information about row counts. There is one entry for each ingestion phase. The definitions of the different row counts are shown below:
*`processed`: Number of rows successfully ingested without parsing errors
*`processedWithError`: Number of rows that were ingested, but contained a parsing error within one or more columns. This typically occurs where input rows have a parseable structure but invalid types for columns, such as passing in a non-numeric String value for a numeric column.
*`thrownAway`: Number of rows skipped. This includes rows with timestamps that were outside of the ingestion task's defined time interval and rows that were filtered out with a [`transformSpec`](index.md#transformspec), but doesn't include the rows skipped by explicit user configurations. For example, the rows skipped by `skipHeaderRows` or `hasHeaderRow` in the CSV format are not counted.
*`unparseable`: Number of rows that could not be parsed at all and were discarded. This tracks input rows without a parseable structure, such as passing in non-JSON data when using a JSON parser.
The `errorMsg` field shows a message describing the error that caused a task to fail. It will be null if the task was successful.
## Live reports
### Row stats
The non-parallel [simple native batch task](../ingestion/native-batch.md#simple-task), the Hadoop batch task, and Kafka and Kinesis ingestion tasks support retrieval of row stats while the task is running.
The live report can be accessed with a GET to the following URL on a Peon running a task:
An example report is shown below. The `movingAverages` section contains 1 minute, 5 minute, and 15 minute moving averages of increases to the four row counters, which have the same definitions as those in the completion report. The `totals` section shows the current totals.
```
{
"movingAverages": {
"buildSegments": {
"5m": {
"processed": 3.392158326408501,
"unparseable": 0,
"thrownAway": 0,
"processedWithError": 0
},
"15m": {
"processed": 1.736165476881023,
"unparseable": 0,
"thrownAway": 0,
"processedWithError": 0
},
"1m": {
"processed": 4.206417693750045,
"unparseable": 0,
"thrownAway": 0,
"processedWithError": 0
}
}
},
"totals": {
"buildSegments": {
"processed": 1994,
"processedWithError": 0,
"thrownAway": 0,
"unparseable": 0
}
}
}
```
For the Kafka Indexing Service, a GET to the following Overlord API will retrieve live row stat reports from each task being managed by the supervisor and provide a combined report.
the generated segments could potentially overshadow each other, which could lead to incorrect query results.
To avoid this problem, tasks will attempt to get locks prior to creating any segment in Druid.
There are two types of locks, i.e., _time chunk lock_ and _segment lock_.
When the time chunk lock is used, a task locks the entire time chunk of a data source where generated segments will be written.
For example, suppose we have a task ingesting data into the time chunk of `2019-01-01T00:00:00.000Z/2019-01-02T00:00:00.000Z` of the `wikipedia` data source.
With the time chunk locking, this task will lock the entire time chunk of `2019-01-01T00:00:00.000Z/2019-01-02T00:00:00.000Z` of the `wikipedia` data source
before it creates any segments. As long as it holds the lock, any other tasks will be unable to create segments for the same time chunk of the same data source.
The segments created with the time chunk locking have a _higher_ major version than existing segments. Their minor version is always `0`.
When the segment lock is used, a task locks individual segments instead of the entire time chunk.
As a result, two or more tasks can create segments for the same time chunk of the same data source simultaneously
if they are reading different segments.
For example, a Kafka indexing task and a compaction task can always write segments into the same time chunk of the same data source simultaneously.
The reason for this is because a Kafka indexing task always appends new segments, while a compaction task always overwrites existing segments.
The segments created with the segment locking have the _same_ major version and a _higher_ minor version.
> The segment locking is still experimental. It could have unknown bugs which potentially lead to incorrect query results.
To enable segment locking, you may need to set `forceTimeChunkLock` to `false` in the [task context](#context).
Once `forceTimeChunkLock` is unset, the task will choose a proper lock type to use automatically.
Please note that segment lock is not always available. The most common use case where time chunk lock is enforced is
when an overwriting task changes the segment granularity.
Also, the segment locking is supported by only native indexing tasks and Kafka/Kinesis indexing tasks.
If you want to unset it for all tasks, you would want to set `druid.indexer.tasklock.forceTimeChunkLock` to false in the [overlord configuration](../configuration/index.md#overlord-operations).
Note that locks are shared by the tasks of the same groupId.
For example, Kafka indexing tasks of the same supervisor have the same groupId and share all locks with each other.
<aname="priority"></a>
## Lock priority
Each task type has a different default lock priority. The below table shows the default priorities of different task types. Higher the number, higher the priority.
|task type|default priority|
|---------|----------------|
|Realtime index task|75|
|Batch index task|50|
|Merge/Append/Compaction task|25|
|Other tasks|0|
You can override the task priority by setting your priority in the task context as below.
```json
"context" : {
"priority" : 100
}
```
<aname="context"></a>
## Context parameters
The task context is used for various individual task configuration. The following parameters apply to all task types.
|`taskLockTimeout`|300000|task lock timeout in millisecond. For more details, see [Locking](#locking).|
|`forceTimeChunkLock`|true|_Setting this to false is still experimental_<br/> Force to always use time chunk lock. If not set, each task automatically chooses a lock type to use. If this set, it will overwrite the `druid.indexer.tasklock.forceTimeChunkLock` [configuration for the overlord](../configuration/index.md#overlord-operations). See [Locking](#locking) for more details.|
|`priority`|Different based on task types. See [Priority](#priority).|Task priority|
|`useLineageBasedSegmentAllocation`|false in 0.21 or earlier, true in 0.22 or later|Enable the new lineage-based segment allocation protocol for the native Parallel task with dynamic partitioning. This option should be off during the replacing rolling upgrade from one of the Druid versions between 0.19 and 0.21 to Druid 0.22 or higher. Once the upgrade is done, it must be set to true to ensure data correctness.|
