Merge pull request #1446 from conradlee/docs-segment-details

Improve segment file documentation

docs/content/design/segments.md

@@ -4,7 +4,125 @@ layout: doc_page
 Segments
 ========
 
-Druid segments contain data for a time interval, stored as separate columns. Dimensions (string columns) have inverted indexes associated with them for each dimension value. Metric columns are LZ4 compressed.
+
+Druid stores its index in *segment files*, which are partitioned by
+time. In a basic setup, one segment file is created for each time
+interval, where the time inteval is configurable in the
+`segmentGranularity` parameter of the `granularitySpec`, which is
+documented [here](../ingestion/batch-ingestion.html).  For druid to
+operate well under heavy query load, it is important for the segment
+file size to be within the recommended range of 300mb-700mb. If your
+segment files are larger than this range, then consider either
+changing the the granularity of the time interval or partitioning your
+data and tweaking the `targetPartitionSize` in your `partitioningSpec`
+(a good starting point for this parameter is 5 million rows).  See the
+sharding section below and the 'Partitioning specification' section of
+the [Batch ingestion](../ingestion/batch-ingestion.html) documentation
+for more information.
+
+### A segment file's core data structures
+Here we describe the internal structure of segment files, which is
+essentially *columnar*: the data for each column is laid out in
+separate data structures. By storing each column separately, Druid can
+decrease query latency by scanning only those columns actually needed
+for a query.  There are three basic column types: the timestamp
+column, dimension columns, and metric columns, as illustrated in the
+image below:
+
+![Druid column types](../img/druid-column-types.png "Druid Column Types")
+
+The timestamp and metric columns are simple: behind the scenes each of
+these is an array of integer or floating point values compressed with
+LZ4. Once a query knows which rows it needs to select, it simply
+decompresses these, pulls out the relevant rows, and applies the
+desired aggregation operator. As with all columns, if a query doesn’t
+require a column, then that column’s data is just skipped over.
+
+Dimensions columns are different because they support filter and
+group-by operations, so each dimension requires the following
+three data structures:
+
+1. A dictionary that maps values (which are always treated as strings) to integer IDs,
+2. For each distinct value in the column, a bitmap that indicates which rows contain that value, and
+3. A list of the column’s values, encoded using the dictionary in 1.
+
+Why these three data structures? The dictionary simply maps string
+values to integer ids so that the values in 2 and 3 can be
+represented compactly. The bitmaps in 2 -- also known as *inverted
+indexes* allow for quick filtering operations (specifically, bitmaps
+are convenient for quickly applying AND and OR operators). Finally,
+the list of values in 3 are needed for *group by* and *TopN*
+queries. In other words, queries that solely aggregate metrics based
+on filters do not need to touch the list of dimension values stored in
+3.
+
+To get a concrete sense of these data structures, consider the ‘page’
+column from the example data above.  The three data structures that
+represent this dimension are illustrated in the diagram below. 
+
+```
+1: Dictionary that encodes column values
+  {
+    "Justin Bieber": 0,
+    "Ke$ha":         1
+  }
+
+2: Column data
+  [0,
+   0,
+   1,
+   1]
+
+3: Bitmaps - one for each unique value of the column
+  value="Justin Bieber": [1,1,0,0]
+  value="Ke$ha":         [0,0,1,1]
+```
+
+Note that the bitmap is different from the first two data structures:
+whereas the first two grow linearly in the size of the data (in the
+worst case), the size of the bitmap section is the product of data
+size * column cardinality. Compression will help us here though
+because we know that each row will have only non-zero entry in a only
+a single bitmap. This means that high cardinality columns will have
+extremely sparse, and therefore highly compressible, bitmaps. Druid
+exploits this using compression algorithms that are specially suited
+for bitmaps, such as roaring bitmap compression.
+
+### Multi-value columns
+
+If a data source makes use of multi-value columns, then the data
+structures within the segment files look a bit different. Let's
+imagine that in the example above, the second row were tagged with
+both the 'Ke$ha' *and* 'Justin Bieber' topics. In this case, the three
+data structures would now look as follows:
+
+```
+1: Dictionary that encodes column values
+  {
+    "Justin Bieber": 0,
+    "Ke$ha":         1
+  }
+
+2: Column data
+  [0,
+   [0,1],  <--Row value of multi-value column can have array of values
+   1,
+   1]
+
+3: Bitmaps - one for each unique value
+  value="Justin Bieber": [1,1,0,0]
+  value="Ke$ha":         [0,1,1,1]
+                            ^
+                            |
+                            |
+    Multi-value column has multiple non-zero entries
+```
+
+Note the changes to the second row in the column data and the Ke$ha
+bitmap. If a row has more than one value for a column, its entry in
+the 'column data' is an array of values. Additionally, a row with *n*
+values in a column columns will have *n* non-zero valued entries in
+that column's bitmaps.
 
 Naming Convention
 -----------------
@@ -17,7 +135,7 @@ datasource_intervalStart_intervalEnd_version_partitionNum
 Segment Components
 ------------------
 
-A segment is comprised of several files, listed below.
+Behind the scenes, a segment is comprised of several files, listed below.
 
 * `version.bin`
 
@@ -52,4 +170,10 @@ Sharding Data to Create Segments
 
 ### Sharding Data by Dimension
 
-If the cumulative total number of rows for the different values of a given column exceed some configurable threshold, multiple segments representing the same time interval for the same datasource may be created. These segments will contain some partition number as part of their identifier. Sharding by dimension reduces some of the the costs associated with operations over high cardinality dimensions.
+If the cumulative total number of rows for the different values of a
+given column exceed some configurable threshold, multiple segments
+representing the same time interval for the same datasource may be
+created. These segments will contain some partition number as part of
+their identifier. Sharding by dimension reduces some of the the costs
+associated with operations over high cardinality dimensions. For more
+information on sharding, see the ingestion documentat