I was working on a demo involving CCIs when I noticed that some of my inserts were taking longer than expected. Table definitions to reproduce:
DROP TABLE IF EXISTS dbo.STG_1048576;
CREATE TABLE dbo.STG_1048576 (ID BIGINT NOT NULL);
INSERT INTO dbo.STG_1048576
SELECT TOP (1048576) ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) RN
FROM master..spt_values t1
CROSS JOIN master..spt_values t2;
DROP TABLE IF EXISTS dbo.CCI_BIGINT;
CREATE TABLE dbo.CCI_BIGINT (ID BIGINT NOT NULL, INDEX CCI CLUSTERED COLUMNSTORE);
For the tests I'm inserting all 1048576 rows from the staging table. That's enough to fill exactly one compressed rowgroup as long as it doesn't get trimmed for some reason.
If I insert all of the integers mod 17000 it takes less than a second:
TRUNCATE TABLE dbo.CCI_BIGINT;
INSERT INTO dbo.CCI_BIGINT WITH (TABLOCK)
SELECT ID % 17000
FROM dbo.STG_1048576
OPTION (MAXDOP 1);
SQL Server Execution Times: CPU time = 359 ms, elapsed time = 364 ms.
However, if I insert the same integers mod 16000 it sometimes takes over 30 seconds:
TRUNCATE TABLE dbo.CCI_BIGINT;
INSERT INTO dbo.CCI_BIGINT WITH (TABLOCK)
SELECT ID % 16000
FROM dbo.STG_1048576
OPTION (MAXDOP 1);
SQL Server Execution Times: CPU time = 32062 ms, elapsed time = 32511 ms.
This is a repeatable test that has been done on multiple machines. There seems to be a clear pattern in elapsed time as the mod value changes:
MOD_NUM TIME_IN_MS
1000 2036
2000 3857
3000 5463
4000 6930
5000 8414
6000 10270
7000 12350
8000 13936
9000 17470
10000 19946
11000 21373
12000 24950
13000 28677
14000 31030
15000 34040
16000 37000
17000 563
18000 583
19000 576
20000 584
If you want to run tests yourself feel free to modify the test code that I wrote here.
I couldn't find anything interesting in sys.dm_os_wait_stats for the mod 16000 insert:
╔════════════════════════════════════╦══════════════╗
║ wait_type ║ diff_wait_ms ║
╠════════════════════════════════════╬══════════════╣
║ XE_DISPATCHER_WAIT ║ 164406 ║
║ QDS_PERSIST_TASK_MAIN_LOOP_SLEEP ║ 120002 ║
║ LAZYWRITER_SLEEP ║ 97718 ║
║ LOGMGR_QUEUE ║ 97298 ║
║ DIRTY_PAGE_POLL ║ 97254 ║
║ HADR_FILESTREAM_IOMGR_IOCOMPLETION ║ 97111 ║
║ SQLTRACE_INCREMENTAL_FLUSH_SLEEP ║ 96008 ║
║ REQUEST_FOR_DEADLOCK_SEARCH ║ 95001 ║
║ XE_TIMER_EVENT ║ 94689 ║
║ SLEEP_TASK ║ 48308 ║
║ BROKER_TO_FLUSH ║ 48264 ║
║ CHECKPOINT_QUEUE ║ 35589 ║
║ SOS_SCHEDULER_YIELD ║ 13 ║
╚════════════════════════════════════╩══════════════╝
Why does the insert for ID % 16000
take so much longer than the insert for ID % 17000
?
Best Answer
In many respects, this is expected behaviour. Any set of compression routines will have widely ranging performance depending on input data distribution. We expect to trade data loading speed for storage size and runtime querying performance.
There is a definite limit to how detailed an answer you're going to get here, since VertiPaq is a proprietary implementation, and the details are a closely-guarded secret. Even so, we do know that VertiPaq contains routines for:
Typically, data will be value or dictionary encoded, then RLE or bit-packing will be applied (or a hybrid of RLE and bit-packing used on different subsections of the segment data). The process of deciding which techniques to apply can involve generating a histogram to help determine how maximum bit savings can be achieved.
Capturing the slow case with Windows Performance Recorder and analyzing the result with Windows Performance Analyzer, we can see that the vast majority of the execution time is consumed in looking at the clustering of the data, building histograms, and deciding how to partition it for best savings:
The most expensive processing occurs for values that appear at least 64 times in the segment. This is a heuristic to determine when pure RLE is likely to be beneficial. The faster cases result in impure storage e.g. a bit-packed representation, with a larger final storage size. In the hybrid cases, values with 64 or more repetitions are RLE encoded, and the remainder are bit-packed.
The longest duration occurs when the maximum number of distinct values with 64 repetitions appear in the largest possible segment i.e. 1,048,576 rows with 16,384 sets of values with 64 entries each. Inspection of the code reveals a hard-coded time limit for the expensive processing. This can be configured in other VertiPaq implementations e.g. SSAS, but not in SQL Server as far as I can tell.
Some insight into the final storage arrangement can be acquired using the undocumented
DBCC CSINDEX
command. This shows the RLE header and array entries, any bookmarks into the RLE data, and a brief summary of the bit-pack data (if any).For more information, see: