So what causes Range Locks? Just ask Sunil. He knows everything (this assumes the serializable isolation level):
If the key value exists, then the range lock is only taken if the index is non-unique. In the non-unique index case, the ‘range’ lock is taken on the requested key and on the ‘next’ key.
If the ‘next’ key does not exist, then a range lock is taken on the ‘infinity’ value. If the index is unique then a regular S lock on the key.
If the key does not exist, then the ‘range’ lock is taken on the ‘next’ key both for unique and non-unique index.
If the ‘next’ key does not exist, then a range lock is taken on the ‘infinity’ value.
Range Predicate (key between the two values)
‘range lock on all the key values in the range when using ‘between’
‘range’ lock on the ‘next’ key that is outside the range. This is true both for unique and non-unique indexes. This is to ensure that no row can be inserted between the requested key and the one after that. If the ‘next’ key does not exist, then a range lock is taken on the ‘infinity’ value.
Erik has an interesting example and lets us see a potential concurrency problem with multi-table indexed views.
There’s a lot going on there, but much of it is noise. There is a whole bunch of contention on the table
SqlPerf.Session— session 342 is trying to perform an update, but it is stuck waiting on shared locks taken by two services. Now, let’s check the Optimize Layout box above, and look at the circular graph again. Simplified, right?
This checkbox is easily the most powerful option to discard noise and help you focus on the crux of the deadlock issue. In the original graph, you can see that many of the elements presented are simply innocent bystanders — waiters that are captured as part of the deadlock activity, but in no way contributing to it. We can detect this in a lot of cases and so, when you check the box, we hide them from view, allowing you to focus much more directly on the key players involved in the deadlock. There is no question that eliminating the noise can really speed up troubleshooting; with those extra nodes removed, I can clearly see that I have some kind of order-of-operations issue on the
SqlPerf.Sessiontable, between the transfer service and the processor service.
The truncate option is fast and efficient but did you know that it takes a certain lock where you could actually be blocked?
What am I talking about? When you issue a truncate it takes a Sch-M lock and it uses this when it is moving the allocation units to the deferred drop queue. So if it takes this lock and you look at the locking compatibility matrix below you will see what can cause a conflict (C).
Arun includes an image which shows what can block what, and also shows us an example.
Troubleshooting of the blocking and concurrency issues is, in the nutshells, a simple process. You need to identify the processes involved in blocking conditions or deadlocks and analyze why those processes acquire the locks on the same resources. In majority of cases, you need to analyze queries and their execution plans identifying possible inefficiencies that led to excessive number of locks being acquired.
Collecting this information is not a trivial task. The information is exposed through DMVs (you can download the set of scripts here); however, it requires you to run the queries at time when blocking occurred. Fortunately, SQL Server allows you to capture blocking and deadlock conditions with the blocked process report and deadlock graph, analyzing them later.
There is the caveat though. Neither blocked process report nor deadlock graph provide you execution plans of the statements. Nor do they always include affected statements in the plain text. You may need to query plan cache and other DMVs to get this information and longer you wait lesser is the chance that the information is available. Moreover, SQL Server may generate enormous number of blocked process reports in cases of prolonged blocking and complex blocking chains, which complicates the analysis.
Confirmed to work with SQL Server 2012 and later, but might work on earlier versions as well. Dmitri has released it to the public, so check it out.
This one isn’t bad, but imagine a multi-statement deadlock, or a server with several deadlocks in an hour – how do you easily see if there were other errors on the server at the same time?
With SQL Server 2012+, we have a better tool to see when deadlocks occur – and the deadlock graphs are saved by default, so we don’t have to read the text version to figure it out, or run a separate trace to capture them.
In SSMS, open Object Explorer and navigate to Extended Events > Sessions > system_health > package0.event_file. Double-click to view the data.
Click through for the entire process.
This is an isolated test system, so I went to clean out Query Store as a reset. I didn’t need any of the old information in there, so I ran:
- ALTER DATABASE BabbyNames SET QUERY_STORE CLEAR ALL;
I was surprised when I didn’t see this complete very quickly, as it normally does.
Click through to see how Kendra diagnoses the issue.
There are two main kinds of SQL queries. SELECT/INSERT/UPDATE/DELETE statements are examples of Data Manipulation Language (DML). CREATE/ALTER/DROP statements are examples of Data Definition Language (DDL).
With schema changes – DDL – we have the added complexity of the SCH-M lock. It’s a kind of lock you don’t see with DML statements. DML statements take and hold schema stability locks (SCH-S) on the tables they need. This can cause interesting blocking chains between the two types where new queries can’t start until the schema change succeeds
Click through for suggestions with regard to schema locks, as well as a few tips for modifying large tables.
In order to update a row, SQL Server first needs to find that row, and only then it can perform the update. So every UPDATE operation is actually split into two phases – first read, and then write. During the read phase, the resource is locked for read, and then it is converted to a lock for write. This is better than just locking for write all the way from the beginning, because during the read phase, other sessions might also need to read the resource, and there is no reason to block them until we start the write phase. We already know that the SHARED lock is used for read operations (phase 1), and that the EXCLUSIVE lock is used for write operations (phase 2). So what is the UPDATE lock used for?
If we used a SHARED lock for the duration of the read phase, then we might run into a deadlock when multiple sessions run the same UPDATE statement concurrently.
Read on for more details.
We’ve only observed this deadlock with multiple concurrent sessions insert to the delta store for the same target CCI due to server memory pressure or very low cardinality estimates (less than 251 rows). The correct mitigation depends on why you’re seeing the issue in the first place. If you’re seeing it due to low cardinality estimates then fix your estimates, or at the very least get them above 250 rows. If you’re seeing them because the memory grant for the CCI build times out after 25 seconds then lower concurrency or increase server memory.
The problem can also be avoided by not doing concurrent inserts in the first place. In some cases a parallel insert may be a reasonable alterative. There’s also some evidence that the deadlock is only seen when the number of rows for insert is very close to 1048576, but we weren’t able to make any definitive conclusions around that.
Read the whole thing. Also check out his Connect item.
It might look complicated but it is actually very simple – query sys.sysprocesses with a cross apply using the sql_handle to get the text of the query, and then an outer apply with the same query again but you are joining to the blocking spid so you can get the text for the query that is doing the blocking. Beyond that, you can filter on various columns and refine your output
When blocking goes bad, it can go really bad. Sometimes it’s because someone (usually, that someone is me) forgets to commit a transaction before going to lunch, and those open locks cause a bunch of blocking. Sometimes a data load runs at a strange time, or an unusual amount of data gets loaded, or a query gets a bad plan and starts running long, or… you get the idea. There are a bunch of reasons this can come up.
The hardest part is that sometimes big blocking chains build up. The session I forgot to commit blocks 5 session. Each of those block 5 sessions. Each of those block 5 sessions… Eventually, I have 8000 sessions waiting on me, and I’m off eating a kale & farro salad. Oops.
The moral of the story is, don’t eat kale and farro salads; that sounds like rabbit food.