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The idea is to optimise the common case where there are long runs of NULL or non-NULL values (i.e. the def level is repeated). We can detect this cheaply by keying the decoding loop in the column reader off the state of the def level RLE decoder - if there's a long run of repeated levels, we can skip checking the def level for every value. We still fall back to decoding, caching and reading value-by-value a batch of def levels whenever the next def level is not in a repeated run. We still use the old approach for decoding rep levels. There might be some benefit to using the same approach for rep levels *if* repeated def and rep level runs line up. These changes should unlock further optimizations because more time is spent in simple kernel functions, e.g. UnpackAndDecode32Values() for dictionary decompression, which is very optimisable using SIMD etc. Snappy decompression now seems to be the main CPU bottleneck for decoding snappy-compressed Parquet. Perf: Running TPC-H scale factor 60 on uncompressed and snappy parquet both showed a ~4% speedup overall. Microbenchmarks on uncompressed parquet show scans only doing dictionary decoding on uncompressed Parquet is ~75% faster: set mt_dop=1; select min(l_returnflag) from lineitem; Testing: We have alltypes agg with a mix of null and non-null. Many tables have long runs of non-null values. Added new test data and coverage: * a test table manynulls with long runs of null values. * a large CHAR test table * missing coverage for materialising pos slot in flattened nested types scan. * Extended dict test to test longer runs. * A larger version of complextypestbl with interesting collection shapes - NULL collections, empty collections, etc, particularly runs of collections with the same shape. * Test interaction of timestamp validation with conversion * Ran code coverage build to confirm all code paths are tested * ASAN and exhaustive runs. Change-Id: I8c03006981c46ef0dae30602f2b73c253d9b49ef Reviewed-on: http://gerrit.cloudera.org:8080/8319 Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com> Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
48 lines
623 B
Plaintext
48 lines
623 B
Plaintext
====
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---- QUERY
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# Test that we materialize the right number of nulls.
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select count(*),
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count(id),
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count(nullcol),
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sum(nullcol)
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from manynulls
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---- RESULTS
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11000,11000,5500,28870000
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---- TYPES
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BIGINT,BIGINT,BIGINT,BIGINT
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====
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---- QUERY
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# Spot check some values.
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select id, nullcol
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from manynulls
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where id >= 4490 and id <= 4510
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order by id
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---- RESULTS
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4490,NULL
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4490,NULL
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4491,NULL
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4492,NULL
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4493,NULL
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4494,NULL
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4495,NULL
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4496,NULL
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4497,NULL
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4498,NULL
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4499,NULL
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4500,4500
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4500,4500
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4501,4501
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4502,4502
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4503,4503
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4504,4504
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4505,4505
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4506,4506
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4507,4507
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4508,4508
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4509,4509
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4510,4510
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4510,4510
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---- TYPES
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INT,INT
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====
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