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https://issues.apache.org/jira/browse/SPARK-17074?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Zhenhua Wang updated SPARK-17074:
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Description:
We support two kinds of histograms:
- Equi-width histogram: We have a fixed width for each column interval in
the histogram. The height of a histogram represents the frequency for those
column values in a specific interval. For this kind of histogram, its height
varies for different column intervals. We use the equi-width histogram when the
number of distinct values is less than 254.
- Equi-height histogram: For this histogram, the width of column interval
varies. The heights of all column intervals are the same. The equi-height
histogram is effective in handling skewed data distribution. We use the equi-
height histogram when the number of distinct values is equal to or greater than
254.
We first use [SPARK-18000] to compute equi-width histograms (for both numeric
and string types) or endpoints of equi-height histograms (for numeric type
only). Then, if we get endpoints of a equi-height histogram, we need to compute
ndv's between those endpoints by [SPARK-17997] to form the equi-height
histogram.
This Jira incorporates three Jiras mentioned above to support needed
aggregation functions. We need to resolve them before this one.
was:
We support two kinds of histograms:
- Equi-width histogram: We have a fixed width for each column interval in
the histogram. The height of a histogram represents the frequency for those
column values in a specific interval. For this kind of histogram, its height
varies for different column intervals. We use the equi-width histogram when the
number of distinct values is less than 254.
- Equi-height histogram: For this histogram, the width of column interval
varies. The heights of all column intervals are the same. The equi-height
histogram is effective in handling skewed data distribution. We use the equi-
height histogram when the number of distinct values is equal to or greater than
254.
We first use [SPARK-18000] and [SPARK-17881] to compute equi-width histograms
(for both numeric and string types) or endpoints of equi-height histograms (for
numeric type only). Then, if we get endpoints of a equi-height histogram, we
need to compute ndv's between those endpoints by [SPARK-17997] to form the
equi-height histogram.
This Jira incorporates three Jiras mentioned above to support needed
aggregation functions. We need to resolve them before this one.
> generate histogram information for column
> -----------------------------------------
>
> Key: SPARK-17074
> URL: https://issues.apache.org/jira/browse/SPARK-17074
> Project: Spark
> Issue Type: Sub-task
> Components: Optimizer
> Affects Versions: 2.0.0
> Reporter: Ron Hu
>
> We support two kinds of histograms:
> - Equi-width histogram: We have a fixed width for each column interval in
> the histogram. The height of a histogram represents the frequency for those
> column values in a specific interval. For this kind of histogram, its height
> varies for different column intervals. We use the equi-width histogram when
> the number of distinct values is less than 254.
> - Equi-height histogram: For this histogram, the width of column interval
> varies. The heights of all column intervals are the same. The equi-height
> histogram is effective in handling skewed data distribution. We use the equi-
> height histogram when the number of distinct values is equal to or greater
> than 254.
> We first use [SPARK-18000] to compute equi-width histograms (for both numeric
> and string types) or endpoints of equi-height histograms (for numeric type
> only). Then, if we get endpoints of a equi-height histogram, we need to
> compute ndv's between those endpoints by [SPARK-17997] to form the
> equi-height histogram.
> This Jira incorporates three Jiras mentioned above to support needed
> aggregation functions. We need to resolve them before this one.
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