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fix typo in segments.md
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@ -82,11 +82,11 @@ Note that the bitmap is different from the first two data structures:
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whereas the first two grow linearly in the size of the data (in the
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worst case), the size of the bitmap section is the product of data
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size * column cardinality. Compression will help us here though
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because we know that each row will have only non-zero entry in a only
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a single bitmap. This means that high cardinality columns will have
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extremely sparse, and therefore highly compressible, bitmaps. Druid
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exploits this using compression algorithms that are specially suited
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for bitmaps, such as roaring bitmap compression.
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because we know that for each row in 'column data', there will only be a
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single bitmap that has non-zero entry. This means that high cardinality
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columns will have extremely sparse, and therefore highly compressible,
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bitmaps. Druid exploits this using compression algorithms that are
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specially suited for bitmaps, such as roaring bitmap compression.
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### Multi-value columns
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@ -121,8 +121,8 @@ data structures would now look as follows:
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Note the changes to the second row in the column data and the Ke$ha
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bitmap. If a row has more than one value for a column, its entry in
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the 'column data' is an array of values. Additionally, a row with *n*
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values in a column columns will have *n* non-zero valued entries in
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that column's bitmaps.
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values in 'column data' will have *n* non-zero valued entries in
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bitmaps.
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Naming Convention
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-----------------
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@ -176,4 +176,4 @@ representing the same time interval for the same datasource may be
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created. These segments will contain some partition number as part of
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their identifier. Sharding by dimension reduces some of the the costs
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associated with operations over high cardinality dimensions. For more
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information on sharding, see the ingestion documentat
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information on sharding, see the ingestion documentation.
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