If you missed the talk, I have everything written down. I have more notes than slides: in the end, I decided to have as few text-only slides as possible, they do not add much to the content anyway.
Slide 1 Introduction
Hi, my name is Konstantin Osipov, I'm a software engineer with Mail.Ru where I develop an open source NoSQL storage server called Tarantool.
Before joining Mail.Ru I was for a long time a server engineer with MySQL, and took part in making MySQL 5.5 happen.
My talk is therefore about the changes we made to server locking in 5.5, why they're beneficial, the performance improvements they brought, and making use of it in conjunction with MySQL PSEA architecture.
Slide 2 Background
People familiar with MySQL development "kitchen" perfectly know that it was often problem-oriented, that is, focused on making existent features work more consistently, improving feature interoperability, rather than developing new features from scratch or re-designing existing ones.
In such environment it often happens that when you begin attacking a problem you don't know where you are going to end, and when you end, it's not the place you probably wanted to be in.
Luckily, and, on the hindsight, I can say this with conviction, MySQL metadata locking turned up a pretty good server system, and, compared to other approaches, an adequate one in the context of the overall server architecture.
I'll try to convince you about that in this talk, but please let me first
Slide 3 Define the problem
In a pure abstraction, it's all about dealing with conflicts between readers and writers. The system doesn't have to be transactional for such conflicts to occur and not in every system such conflicts need to be resolved by means of a locking mechanism.
A concrete example is a concurrent execution of UPDATE and ALTER TABLE statements on the same table.
When executing it in parallel, the RDBMS needs to protect not only the consistency of data on disk, but the practical consistency of shared data structures in main memory, various caches, and so on.
Whoops, but thusly defined,
Slide 4 The problem doesn't exist
or is part of a bigger picture.
Indeed, in a classic RDBMS, the server doesn't specifically protect DML from DDL statements. Naturally, as long as the catalog data is represented just the same as any other data - in a relational table, the DBMS can use the same mechanism to protect catalog data as it uses for any other relation.
An example: a row-based locking engine.
Slide 5 PostgreSQL approach
In this vein, PostgreSQL consistently uses MVCC to access a catalog table like it does for any other relation. Since each transaction has its own consistent read view of the database, a DML statement sees a snapshot of catalog data, and DDL can be fully online.
One may wonder, how can one implement replication in this environment. After all, to apply changes to the slave, one needs to come up with a serial order of all transactions running in the system (this is what the binary log, after all, is: a serial order of all transactions), and such order would not be possible to come up with in a consistent read- based server.
Unlike MySQL, and similarly to some of the MySQL storage engines, PostgreSQL employs physical replication: the binary log contains information about files and blocks, not tables and records. Therefore, statement-based replication will, most likely, never be done in PostgreSQL.
Slide 6 The realm of MySQL architecture
The elegance of a uniform approach to locking of data and metadata is out of reach for a complicated and heterogeneous RDBMS, such as MySQL.
Indeed, the server needs to maintain consistency not just between table data and catalog data in one MVCC, but between multiple storage engines, engines and the binary log, data and the query cache, or, ultimately, multiple MySQL server nodes in MySQL cluster.
On top of that, not all engines support transactions or row locks (only table level locks), some engines don't support their own catalog of data.
(A diagram with MySQL subsystems)
Maintaining consistency between all these subsystems had a lot of holes: and here's the proof:
Slide 8 List of bugs dealt with by the new locking
Overall, we can group these problems in these categories:
Slide 9 In come the metadata locks
MySQL needed a stand-alone locking mechanism, usable with all engines and by all participants. We needed a single system that could be used to protect a single table, for usual ALTER/DROP/CREATE, a schema, for statements like RENAME DATABASE, or the entire instance, in cases such as SET GLOBAL READ ONLY.
It should be possible to take a lock only based on object name, with no knowledge of the underlying storage engine, without having to access any data structures, plug-ins or other objects -- because such data structures or plug-ins may themselves require protection by metadata locking.
It should be possible to gradually acquire one lock after another, without risk of a deadlock: take a lock on a table based on its name, open its definition, discover that it's a view, and go on with locking of the underlying view objects.
It should be possible to specify lock duration, for short-lived lock, such as a single statement, normal locks, lasting from transaction start to end, and special MySQL locks, lasting indefinitely long -- those taken by LOCK TABLES and HANDLER statements, and all that without causing too much deadlocks.
Slide 10 What a metadata lock is
A metadata lock thus is identified by a type and a name. The system can support arbitrary types, and logical nesting of object types (e.g. tables belong to schemas). Lock compatibility matrix for each type may differ.
Each lock has a list of pending and granted tickets. A ticket identifies the thread, which was granted the lock, and the lock type -- the same lock object may be granted to many threads, if their request types are compatible with each other.
Slide 11 How this solves the problem
A metadata lock is taken on each name prior to the use of the named object. If an object is part of a hierarchy, an intention lock is taken on the parent until we reach the scope of the universe.
Lock duration allows MDL to automatically free locks at statement end, transaction end, and so on.
While we still employ deadlock avoidance techniques, deadlock detection is a saviour for cases when lock order is ad-hoc, or try-and-back-off approach could not be used.
Slide 12 Hierarchical locks
MDL supports hierarchical locks. They allow one to lock an entire instance or an individual schema.
BACKUP DATABASE no longer needs to take down the entire instance Online backup is deadlock-detection aware
Slide 13 The comforts of deadlock detection
MySQL used to employ lock avoidance techniques that worked in 99% of cases (c) Monty.
However, with longer lock durations, lock requests can come in ad-hoc order: some deadlock detection mechanism becomes mandatory.
Besides, just taking your chances, rather than trying to avoid a deadlock, makes the most used case simpler and more efficient.
What is our deadlock detector?
We feature a graph-based deadlock detection. There may be parallel searches in the graph going on a the same time, and each search would inspect the relevant part of the subgraph.
Even though the problem of finding a cycle in a graph is NP-complete, and we employ a simple breadth-first search, the practical performance is excellent, since most often the wait-for graph in MDL consists of a large number of disjoint and small subgraphs.
When choosing a victim, we look at lock priority: DDL is more important than DML, and DML usually gets aborted.
Slide 14 Performance
There is a myth in C/C++ developers community that mutexes are a danger and should not be used, which was further inflated by mass adoption by lock-free algorithms. The lesson we learned with MDL is that mutexes aren't "evil": it's important to make sure that the mutex is not contended, and that the critical path uses a moderate amount of mutexes (each unlock is a CPU cache flush). If these two precautions are made, mutexes can be used to build highly scalable apps.
MDL at it start received some criticism, such as it adds a new subsystem, a new mutex on the critical path. It should be understood that this is a replacement mutex: the goal is to unify all locks and MDL makes it possible. MDL can even wrap other mutexes into its locks, and thus make the mutex order deadlocks visible to the deadlock detector.
The other piece of criticism was that the graph-based deadlock detection is very slow, especially when you have a thousand of nodes, and the problem finding a loop in this grpah is NP complete. In practice, however, our graph is very disjoint, and typical deadlock cycle length is under 4. Setting a cap on the depth of the search solves the complexity problem and is a very acceptable solution in the circumstances.
Slide 15 How to plug in
You need to plug-in to let MySQL detect cross-storage engine deadlocks.
We don't dictate how you represent your wait-for subgraph: this allows various subsystem to use simpler implementations, or re-use their existing data structures.
We have stored our data structure that represents the wait-for graph in a thread local data. It's not a standalone data structure: it's simple, since there is a place for only one verge. The locks are taken one at a time, so we do not need to store a complicated graph.
To plug in: