Github user paul-rogers commented on a diff in the pull request:
https://github.com/apache/drill/pull/886#discussion_r129728610
--- Diff: _docs/performance-tuning/010-performance-tuning-introduction.md
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@@ -3,9 +3,9 @@ title: "Performance Tuning Introduction"
date:
parent: "Performance Tuning"
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-You can apply performance tuning measures to improve how efficiently Drill
queries data. To significantly improve performance in Drill, you must have
knowledge about the underlying data and data sources, as well as familiarity
with how Drill executes queries.
+You can change system options in Drill to improve the query performance.
Before you improve performance in Drill, you must choose a layout of the data
and the choose an appropriate file format specific to your use case. For
example, for an analytic workload operating on historical time series data,
then choosing Parquet as the file format and a partitioning scheme that uses
time as a partitionining dimension would be a recommended approach. In the case
you are directly querying data data sources, you need to have an understanding
of the data source itself. Some familiarity with how Drill executes queries can
also help.
-You can analyze query plans and profiles to identify the source of
performance issues in Drill. Once you have isolated the source of an issue, you
can apply the following tuning techniques to improve query performance:
+You can analyze query plans and profiles to identify performance
bottlenecks in Drill. Once you identified issue, here are a couple of best
practices to get you started:
--- End diff --
"Once you identified issue" --> "Once you have identified an issue"
Actually, the original wording flows better...
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