I will double my all-time total of public speaking engagements this September at the MySQL Connect conference in San Fransisco.

The title of my session is "Delivering Breakthrough Performance with MySQL Cluster", and it's between 17:30 and 18:30 on Saturday 29th September.

The content is not finalised yet, so if there's something you would like to hear about which fits with the abstract, then comment below.  If it doesn't fit in with the




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tldr; A single MySQL Cluster prioritises Consistency in Network partition events. Asynchronously replicating MySQL Clusters prioritise Availability in Network partition events.

I was recently asked about the relationship between MySQL Cluster and the CAP theorem. The CAP theorem is often described as a pick two out of three problem, such as choosing from good, cheap, fast. You can have any two, but you can't have all three. For CAP the three qualities are 'Consistency', 'Availability' and 'Partition tolerance'. CAP states that in a system with data replicated over a network only two of these three qualities can be maintained at once, so which two does MySQL Cluster provide?

Standard 'my interpretation of CAP' section

Everyone who discusses CAP like to rehash





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As always, I am a little late, but I want to jump on the bandwagon and mention the recent MySQL Cluster milestone of passing 1 billion queries per minute. Apart from echoing the arbitrarily large ransom demand of Dr Evil, what does this mean?

Obviously 1 billion is only of interest to us humans as we generally happen to have 10 fingers, and seem to name multiples in steps of 10^3 for some reason. Each processor involved in this benchmark is clocked at several billion cycles per second, so a single billion is not so vast or fast.

Measuring over a minute also feels unnatural for a computer performance benchmark - we are used to lots of things happening every second! A minute is a long time in silicon.

What's





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As promised, this is the final post in a series looking at eventual consistency with MySQL Cluster asynchronous replication. This time I'll describe the transaction dependency tracking used with NDB$EPOCH_TRANS and review some of the implementation properties.

Transaction based conflict handling with NDB$EPOCH_TRANS

NDB$EPOCH_TRANS is almost exactly the same as NDB$EPOCH, except that when a conflict is detected on a row, the whole user transaction which made the conflicting row change is marked as conflicting, along with any dependent transactions. All of these rejected row operations are then handled using






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In previous posts I described how row conflicts are detected using epochs. In this post I describe how they are handled.

Row based conflict handling with NDB$EPOCH

Once a row conflict is detected, as well as rejecting the row change, row based conflict handling in the Slave will :

• Increment conflict counters
• Optionally insert a row into an exceptions table
  

For NDB$EPOCH, conflict detection and handling operates on one Cluster in an Active-Active pair designated as the Primary. When a Slave MySQLD attached to the Primary Cluster detects a conflict between data stored in the








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The last post described MySQL Cluster epochs and why they provide a good basis for conflict detection, with a few enhancements required. This post describes the enhancements.

The following four mechanisms are required to implement conflict detection via epochs :

Slaves should 'reflect' information about replicated epochs they have applied
Applied epoch numbers should be included in the Slave Binlog events returning to the originating cluster, in a Binlog position corresponding to the commit time of the

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Before getting to the details of how eventual consistency is implemented, we need to look at epochs. Ndb Cluster maintains an internal distributed logical clock known as the epoch, represented as a 64 bit number. This epoch serves a number of internal functions, and is atomically advanced across all data nodes.

Epochs and consistent distributed state

Ndb is a parallel database, with multiple internal transaction coordinator components starting, executing and committing transactions against rows stored in different data nodes. Concurrent transactions only interact where they attempt to lock the same row. This






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In my previous posts I introduced two new conflict detection functions, NDB$EPOCH and NDB$EPOCH_TRANS without explaining how these functions actually detect conflicts? To simplify the explanation I'll initially consider two circularly replicating MySQL Servers, A and B, rather than two replicating Clusters, but the principles are the same.

Commit ordering

Avoiding conflicts requires that data is only modified on one Server at






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I've already described Justin Swanhart's Flexviews project as something I think is cool. Since then Justin appears to have been working more on Shard-Query which I also think is cool, perhaps even more so than Flexviews.

On the page linked above, Shard-Query is described using the following statements :

"Shard-Query is a distributed parallel query engine for MySQL"

"ShardQuery is a PHP class which is intended to make working with a partitioned dataset easier"

"ParallelPipelining - MPP distributed query engines runs fragments of queries in parallel, combining the results at the end. Like map/reduce except it speaks SQL

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In my last post I described the motivation for the new NDB$EPOCH conflict detection function in MySQL Cluster. This function detects when a row has been concurrently updated on two asynchronously replicating MySQL Cluster databases, and takes steps to keep the databases in alignment.

With NDB$EPOCH, conflicts are detected and handled on a row granularity, as opposed to column granularity, as this is the granularity of the epoch metadata used to detect conflicts. Dealing




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tl;dr : New 'automatic' optimistic conflict detection functions available giving the best of both optimistic and pessimistic replication on the same data

MySQL replication supports a number of topologies, and one of the most interesting is an active-active, or master-master topology, where two or more Servers accept read and write traffic, with asynchronous replication between them.

This topology has a number of attractions, including :

• Potentially higher availability
• Potentially low impact on read/write latency
• Service availability insensitive to replication

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Unlike most other MySQL storage engines, Ndb does not perform all of its work in the MySQLD process. The Ndb table handler maps Storage Engine Api calls onto NdbApi calls, which eventually result in communication with data nodes. In terms of layers, we have SQL -> Handler Api -> NdbApi -> Communication. At each of these layer boundaries, the mapping between operations at the upper layer to operations at the lower layer is non trivial, based on runtime state, statistics, optimisations etc.

The MySQL status variables can be used to understand the behaviour of the MySQL Server in terms of user commands processed, and also how these map to some of the Storage Engine Handler Api calls.

Status variables



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Most people looking at a diagram showing the Cluster architecture soon want to know if the system can scale online. Api nodes such as MySQLD processes can be added online, and the storage capacity of existing data nodes can be increased online, but it was not always possible to add new data nodes to the cluster without an initial system restart requiring a backup and restore.

An online add node and data repartitioning feature was finally implemented in MySQL Cluster 7.0. It's not clear how often users actually do scale their Clusters online, but it certainly is a cool thing to be able to do.

There are two parts to the feature :

Online add an empty data node to an existing cluster

Online rebalance existing data across the existing and new data nodes

Adding an empty data node to a cluster sounds trivial, but is actually fairly





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MySQL Cluster distributes rows amongst the data nodes in a cluster, and also provides data replication. How does this work? What are the trade offs?

Table fragments

Tables are 'horizontally fragmented' into table fragments each containing a disjoint subset of the rows of the table. The union of rows in all table fragments is the set of rows in the table. Rows are always identified by their primary key. Tables with no primary key are given a hidden primary key by MySQLD.

By default, one table fragment is created for each data node in the cluster at the time the table is created.

Node groups and Fragment replicas

The data nodes in a cluster are logically divided into Node groups. The size of each Node group is controlled by the NoOfReplicas parameter. All data nodes in a Node group store the same data. In









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When MySQL AB bought Sun Microsystems in 2008 (or did Sun buy MySQL?), most of the MySQL team merged with the existing Database Technology Group (DBTG) within Sun. The DBTG group had been busy working on JavaDB, Postgres and other DB related projects as well as 'High Availability DB' (HADB), which was Sun's name for the database formerly known as Clustra.

Clustra originated as a University research project which spun out into a startup company and was then acquired by Sun around the era of dot-com. A number of technical papers describing aspects of Clustra's design and history can be found online, and it is in many ways similar to Ndb Cluster, not just in their shared Scandinavian roots. Both are shared-nothing parallel databases originally aimed at the Telecoms market, supporting high availability

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One thing that has puzzled me about MySQL Server is that it became famous for sharded scale-out deployments in well known web sites and yet has no visible support for such deployments. The MySQL killer feature for some time has been built-in asynchronous replication and gigabytes of blogs have been written about how to setup, use, debug and optimise replication, but when it comes to 'sharding' there is nothing built in. Perhaps to have attempted to implement something would have artificially constrained user's imaginations, whereas having no support at all has allowed 1,000 solutions to sprout? Perhaps there just wasn't MySQL developer bandwidth available, or perhaps it just wasn't the best use of the available time. In any case, it remains unclaimed territory to this day.

On first hearing of the

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Most software people are aware of the ACID acronym coined by Jim Gray. With the growth of the web and open source, the scaling and complexity constraints imposed on DBMS implementations supporting ACID are more visible, and new (or at least new terms for known) compromises and tradeoffs are being discussed widely. The better known NoSQL systems are giving insight by example into particular choices of tradeoffs.

Working at MySQL, I have often been surprised at the variety of potential alternatives when implementing a DBMS, and the number of applications which don't need the full set of ACID letters in the strictest form. The original MySQL storage engine, MyISAM is one of the first and most successful examples of

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I'm sure that someone else can describe the actual history of Ndb development much better, but here's my limited and vague understanding.

• Ndb is developed in an environment (Ericsson AXE telecoms switch) where Ericsson's PLEX is the language of choice
  PLEX supports multiple state machines (known as blocks) sending messages (known as signals) between them with some system-level conventions for starting up, restart and message classes. Blocks maintain internal state and define signal handling routines for different signal types. Very little abstraction within a block beyond subroutines is supported. (I'd love to hear some more detail on PLEX and how it has evolved). This architecture maps directly to the AXE processor
  

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MySQL Cluster (http://www.mysql.com/products/database/cluster/) is the name given to one or more MySQL Server processes, connected to an Ndb Cluster database. From the point of view of the MySQL Server processes, the Ndb Cluster is a Storage Engine, implementing transactional storage of tables containing rows. From the point of view of the Ndb Cluster database, the MySQL Server processes are API nodes, performing DDL and DML transactions on tables stored in the cluster. Both exist independently – Ndb Cluster can be used without attached MySQL Server processes, but almost all users of Ndb Cluster connect at least one MySQL Server for DDL and administration.

Ndb stands for Network DataBase. This is a telecoms phrase where Network usually refers to a fixed or wireless telephone

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