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| <h1 id="schema-changes">Schema Changes</h1> |
| |
| <p>Schemas for datasources can change at any time and Apache Druid (incubating) supports different schemas among segments.</p> |
| |
| <h2 id="replacing-segments">Replacing Segments</h2> |
| |
| <p>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.</p> |
| <div class="highlight"><pre><code class="language-text" data-lang="text"><span></span>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 |
| </code></pre></div> |
| <p>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.</p> |
| <div class="highlight"><pre><code class="language-text" data-lang="text"><span></span>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 |
| </code></pre></div> |
| <p>Druid batch indexing (either Hadoop-based or IndexTask-based) guarantees atomic updates on an interval-by-interval basis. |
| In our example, until all <code>v2</code> segments for <code>2015-01-01/2015-01-02</code> are loaded in a Druid cluster, queries exclusively use <code>v1</code> segments. |
| Once all <code>v2</code> segments are loaded and queryable, all queries ignore <code>v1</code> segments and switch to the <code>v2</code> segments. |
| Shortly afterwards, the <code>v1</code> segments are unloaded from the cluster.</p> |
| |
| <p>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:</p> |
| <div class="highlight"><pre><code class="language-text" data-lang="text"><span></span>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 |
| </code></pre></div> |
| <p><code>v2</code> segments will be loaded into the cluster as soon as they are built and replace <code>v1</code> segments for the period of time the |
| segments overlap. Before v2 segments are completely loaded, your cluster may have a mixture of <code>v1</code> and <code>v2</code> segments.</p> |
| <div class="highlight"><pre><code class="language-text" data-lang="text"><span></span>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 |
| </code></pre></div> |
| <p>In this case, queries may hit a mixture of <code>v1</code> and <code>v2</code> segments.</p> |
| |
| <h2 id="different-schemas-among-segments">Different Schemas Among Segments</h2> |
| |
| <p>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.</p> |
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