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 Primary and a replicated event …

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 :

1. 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 replicated epoch …

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 design minimises unnecessary system-wide …

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 a time. …

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 directly."

The things I like from the above description :

• Distributed
…

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 with conflicts on a …

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 failures
• Conceptually simple

…

The HandlerSocket project is described in Yoshinori Matsunobu's blog entry under the title 'Using MySQL as a NoSQL - A story for exceeding 750,000 qps on a commodity server'. It's a great headline and has generated a lot of buzz. Quite a few early commentators were a little confused about what it was - a new NoSQL system using InnoDB? A cache? In memory only? Where does Memcached come in? Does it support the Memcached protocol? If not, why not? Why is it called HandlerSocket?

Inspirations from Memcache may include the focus on simplicity, performance and a simple human readable protocol. As Yoshinori says, Kazuho Oku has already implemented a MySQLD-embedded Memcached server, no need to do it again. What's more, the Memcache protocol …

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 tracking user commands start with …

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 :

1. Online add an empty data node to an existing cluster
2. 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 complex given the cluster's …