Note: After you apply CU 16 for SQL Server 2016 SP2, you might encounter an issue in which DML (insert/update/delete) queries that use parallel plans cannot complete any execution and encounter HP_SPOOL_BARRIER waits. You can use the trace flag 13116 or MAXDOP=1 hint to work around this issue. This issue is related to the introduction of fix for 13685819 and it will be fixed in the next Cumulative Update.
That warning links to bug reference 13685819 on the same page. There isn’t a separate KB article, only the description:
Fixes an issue with insert query in SQL Server 2016 that reads the data from the same table and uses a parallel execution plan may produce duplicate rows
In When Do SQL Server Sorts Rewind? I described how most sorts can only rewind when they contain at most one row. The exception is in-memory sorts, which can rewind at most 500 rows and 16KB of data.
These are certainly tight restrictions, but we can still make use of them on occasion.
To illustrate, I am going reuse a demo Itzik Ben-Gan provided in part one of his Closest Match series, specifically solution 2 (modified value range and indexing).
As Itzik’s title suggests, the task is to find the closest match for a value in one table in a second table.
As Itzik describes it:
The challenge is to match to each row from T1 the row from T2 where the absolute difference between T2.val and T1.val is the lowest. In case of ties (multiple matching rows in T2), match the top row based on val ascending, keycol ascending order.
That is, the row with the lowest value in the val column, and if you still have ties, the row with the lowest keycol value. The tiebreaker is used to guarantee determinism.
The SQL Server 2019 query optimizer has a new trick available to improve the performance of large aggregations. The new exploration abilities are encoded in two new closely-related optimizer rules:
GbAggSplitToRanges
SelOnGbAggSplitToRanges
The extended event query_optimizer_batch_mode_agg_split is provided to track when this new optimization is considered. The description of this event is:
Occurs when the query optimizer detects batch mode aggregation is likely to spill and tries to split it into multiple smaller aggregations.
Other than that, this new feature hasn’t been documented yet. This article is intended to help fill that gap.
A bug with Halloween Protection and the OUTPUT Clause
Background
The OUTPUT clause can be used to return results from an INSERT, UPDATE, DELETE, or MERGE statement. The data can be returned to the client, inserted to a table, or both.
There are two ways to add OUTPUT data to a table:
Using OUTPUT INTO
With an outer INSERT statement.
For example:
-- Test tableDECLARE@Targettable(
id integerIDENTITY(1,1)NOTNULL,c1integerNULL);-- Holds rows from the OUTPUT clauseDECLARE@Outputtable(
id integerNOTNULL,c1integerNULL);
One of the transformations available to the SQL Server query optimizer is pulling a logical Group By (and any associated aggregates) above a Join.
Visually, this means transforming a tree of logical operations from:
…to this:
The above diagrams are logical representations. They need to be implemented as physical operators to appear in an execution plan. The options are:
Group By
Hash Match Aggregate
Stream Aggregate
Distinct Sort
Join
Nested Loops Join
Nested Loops Apply
Hash Match Join
Merge Join
When the optimizer moves a Group By above a Join it has to preserve the semantics. The new sequence of operations must be guaranteed to return the same results as the original in all possible circumstances.
One cannot just pick up a Group By and arbitrarily move it around the query tree without risking incorrect results.
This is a companion post to my main article Batch Mode Bitmaps in SQL Server. This post provides demos and illustrations to supplement the technical article.
The scripts presented here were run on SQL Server 2017 CU 16.
SQL is a declarative language. We use SQL to write a logical query specification that defines the results we want. For example, we might write a query using either APPLY or JOIN that logically describes exactly the same results.
It is up to the query optimizer to find an efficient physical implementation of that logical requirement. SQL Server is free to choose any plan it likes, so long as the results are guaranteed to be the same as specified in the original SQL.
The optimizer is capable of transforming an apply to a join and vice versa. It generally tries to rewrite apply to join during initial compilation to maximize the searchable plan space during cost-based optimization. Having transformed an apply to a join early on, it may also consider a transformation back to an apply shape later on to assess the merits of e.g. an index loops join.
Creating a table is a relatively resource-intensive and time-consuming operation. The server must locate and allocate storage space for the new data and index structures and make the corresponding entries in multiple system metadata tables. All this work has to be done in ways that will always work correctly under high concurrency, and which meet all of the ACID guarantees expected of a relational database.
In SQL Server, this means taking the right kinds of locks and latches, in the correct sequence, while also ensuring that detailed transaction log entries are safely committed to persistent storage in advance of any physical changes to the database. These log entries ensure the system can bring the database back to a consistent state in the event of a transaction rollback or system crash.
Dropping a table is a similarly expensive operation. Luckily, most databases do not create or drop tables with any great frequency. The obvious exception to this is the system database tempdb. This single database contains the physical storage, allocation structures, system metadata, and transaction log entries for all temporary tables and table variables across the entire SQL Server instance.
It is in the nature of temporary tables and table variables to be created and dropped much more frequently than other database object types. When this naturally high frequency of creation and destruction is combined with the concentrating effect of all temporary tables and table variables being associated with a single database, it is hardly surprising that contention can arise in the allocation and metadata structures of the tempdb database.