identifies segments using the datasource, interval, version, and partition number. The partition number is only visible in the segment id if
there are multiple segments created for some granularity of time. For example, if you have hourly segments, but you
have more data in an hour than a single segment can hold, you can create multiple segments for the same hour. These segments will share
the same datasource, interval, and version, but have linearly increasing partition numbers.
```
foo_2015-01-01/2015-01-02_v1_0
foo_2015-01-01/2015-01-02_v1_1
foo_2015-01-01/2015-01-02_v1_2
```
In the example segments above, the dataSource = foo, interval = 2015-01-01/2015-01-02, version = v1, partitionNum = 0.
If at some later point in time, you reindex the data with a new schema, the newly created segments will have a higher version id.
```
foo_2015-01-01/2015-01-02_v2_0
foo_2015-01-01/2015-01-02_v2_1
foo_2015-01-01/2015-01-02_v2_2
```
Druid batch indexing (either Hadoop-based or IndexTask-based) guarantees atomic updates on an interval-by-interval basis.
In our example, until all `v2` segments for `2015-01-01/2015-01-02` are loaded in a Druid cluster, queries exclusively use `v1` segments.
Once all `v2` segments are loaded and queryable, all queries ignore `v1` segments and switch to the `v2` segments.
Shortly afterwards, the `v1` segments are unloaded from the cluster.
Note that updates that span multiple segment intervals are only atomic within each interval. They are not atomic across the entire update.
For example, you have segments such as the following:
```
foo_2015-01-01/2015-01-02_v1_0
foo_2015-01-02/2015-01-03_v1_1
foo_2015-01-03/2015-01-04_v1_2
```
`v2` segments will be loaded into the cluster as soon as they are built and replace `v1` segments for the period of time the
segments overlap. Before v2 segments are completely loaded, your cluster may have a mixture of `v1` and `v2` segments.
```
foo_2015-01-01/2015-01-02_v1_0
foo_2015-01-02/2015-01-03_v2_1
foo_2015-01-03/2015-01-04_v1_2
```
In this case, queries may hit a mixture of `v1` and `v2` segments.
### Different schemas among segments
Druid segments for the same datasource may have different schemas. If a string column (dimension) exists in one segment but not
another, queries that involve both segments still work. Queries for the segment missing the dimension will behave as if the dimension has only null values.
Similarly, if one segment has a numeric column (metric) but another does not, queries on the segment missing the
metric will generally "do the right thing". Aggregations over this missing metric behave as if the metric were missing.
<aname="compact"></a>
## Compaction and reindexing
Compaction is a type of overwrite operation, which reads an existing set of segments, combines them into a new set with larger but fewer segments, and overwrites the original set with the new compacted set, without changing the data that is stored.
For performance reasons, it is sometimes beneficial to compact a set of segments into a set of larger but fewer segments, as there is some per-segment processing and memory overhead in both the ingestion and querying paths.
Compaction tasks merge all segments of the given interval. The syntax is:
|`dimensionsSpec`|Custom dimensionsSpec. Compaction task will use this dimensionsSpec if exist instead of generating one. See below for more details.|No|
|`metricsSpec`|Custom metricsSpec. Compaction task will use this metricsSpec if specified rather than generating one.|No|
|`segmentGranularity`|If this is set, compactionTask will change the segment granularity for the given interval. See `segmentGranularity` of [`granularitySpec`](index.md#granularityspec) for more details. See the below table for the behavior.|No|
To control the number of result segments per time chunk, you can set [maxRowsPerSegment](../configuration/index.md#compaction-dynamic-configuration) or [numShards](../ingestion/native-batch.md#tuningconfig).
Please note that you can run multiple compactionTasks at the same time. For example, you can run 12 compactionTasks per month instead of running a single task for the entire year.
A compaction task internally generates an `index` task spec for performing compaction work with some fixed parameters.
- Dimensions: since Apache Druid supports schema change, the dimensions can be different across segments even if they are a part of the same dataSource.
If the input segments have different dimensions, the output segment basically includes all dimensions of the input segments.
However, even if the input segments have the same set of dimensions, the dimension order or the data type of dimensions can be different. For example, the data type of some dimensions can be
changed from `string` to primitive types, or the order of dimensions can be changed for better locality.
In this case, the dimensions of recent segments precede that of old segments in terms of data types and the ordering.
This is because more recent segments are more likely to have the new desired order and data types. If you want to use
your own ordering and types, you can specify a custom `dimensionsSpec` in the compaction task spec.
- Roll-up: the output segment is rolled up only when `rollup` is set for all input segments.
Druid can insert new data to an existing datasource by appending new segments to existing segment sets. It can also add new data by merging an existing set of segments with new data and overwriting the original set.
Druid does not support single-record updates by primary key.
1. The segment must first be marked as "unused". This occurs when a segment is dropped by retention rules, and when a user manually disables a segment through the Coordinator API.
2. After segments have been marked as "unused", a Kill Task will delete any "unused" segments from Druid's metadata store as well as deep storage.
For documentation on retention rules, please see [Data Retention](../operations/rule-configuration.md).
For documentation on disabling segments using the Coordinator API, please see the
A data deletion tutorial is available at [Tutorial: Deleting data](../tutorials/tutorial-delete-data.md)
## Kill Task
Kill tasks delete all information about a segment and removes it from deep storage. Segments to kill must be unused (used==0) in the Druid segment table. The available grammar is:
Druid supports retention rules, which are used to define intervals of time where data should be preserved, and intervals where data should be discarded.
Druid also supports separating Historical processes into tiers, and the retention rules can be configured to assign data for specific intervals to specific tiers.
These features are useful for performance/cost management; a common use case is separating Historical processes into a "hot" tier and a "cold" tier.
For more information, please see [Load rules](../operations/rule-configuration.md).
