Schemas for datasources can change at any time and Druid supports different schemas among segments.
Druid uniquely 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.
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.