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Showing entries 1 to 9

Displaying posts with tag: NDB API (reset)

Scalable, persistent, HA NoSQL Memcache storage using MySQL Cluster
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Memcached API with Cluster Data Nodes

The native Memcached API for MySQL Cluster is now GA as part of MySQL Cluster 7.2

This post was first published in April 2011 when the first trial version of the Memcached API for MySQL Cluster was released; it was then up-versioned for the second MySQL Cluster 7.2 Development Milestone Release in October 2011. I’ve now refreshed the post based on the GA of MySQL Cluster 7.2 which includes the completed Memcache API.

There are a number of attributes

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Using ClusterJ (part of MySQL Cluster Connector for Java) – a tutorial
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Fig. 1 Java access to MySQL Cluster

ClusterJ is part of the MySQL Cluster Connector for Java which is currently in beta as part of MySQL Cluster 7.1. It is designed to provide a high performance method for Java applications to store and access data in a MySQL Cluster database. It is also designed to be easy for Java developers to use and is “in the style of” Hibernate/Java Data Objects (JDO) and JPA. It uses the Domain Object Model DataMapper pattern:

  • Data is represented as domain objects
  • Domain objects are separate from business logic
  • Domain objects are mapped to database tables

The purpose of ClusterJ is to

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MySQL Cluster Connector for Java – replay available for part 1 of the webinar
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The replay of the two webinars can now be accesed from mysql.com (http://www.mysql.com/news-and-events/on-demand-webinars/display-od-487.html)

Remember that the second part of the webinar will be on March 3rd (details below).

ClusterJ Architecture

MySQL have been working on a new way of accessing MySQL Cluster using Java. Designed for Java developers, the MySQL Cluster Connector for Java implements an easy-to-use and high performance native Java interface and OpenJPA plug-in that maps Java classes to tables stored in the high availability, real-time MySQL Cluster database.

There is a series of 2 webinars coming up, as always

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New white paper: Guide to Optimizing Performance of the MySQL Cluster Database
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MySQL Cluster Connection Pooling

This guide explores how to tune and optimize the MySQL Cluster database to handle diverse workload requirements. It discusses data access patterns and how to build distribution awareness into applications, before exploring schema and query optimization, tuning of parameters and how to get the best out of the latest innovations in hardware design.

The Guide concludes with recent performance benchmarks conducted with the MySQL Cluster database, an overview of how MySQL Cluster can be integrated with other MySQL storage engines, before summarizing additional resources that will enable you to optimize MySQL Cluster

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Using NDB API Events to mask/hide colum data when replicating
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If you  have asynchronous replication where the slave database is using MySQL Cluster then you can use the NDB API events functionality to mask/overwrite data. You might do this for example if the replica is to be used for generating reports where some of the data is sensitive and not relevant to those reports. Unlike stored procedures, NDB API events will be triggered on the slave.

The first step is to set up replication (master->slave rather than multi-master) as described in Setting up MySQL Asynchronous Replication for High Availability).

In this example, the following table definition is used:

mysql> use clusterdb;
mysql> create table ASSETS (CODE int not null primary key, VALUE
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Doxygen output for MySQL Cluster NDB API & MGM API
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NDB API Documentation


A new page has been added to this site: NDB API Docs which presents the information from the header files for both the NDB API and the NDB Management API.

The material has been generated using doxygen and will be refreshed shortly after any new major, minor or maintenance release is made generally available (starting from MySQL Cluster 7.0.6).

Intelligent user-controlled partitioning and writing distribution-aware NDB API Applications
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Default partitioning

By default, Cluster will partition based on primary key

When adding rows to a table that’s using MySQL Cluster as the storage engine, each row is assigned to a partition where that partition is mastered by a particular data node in the Cluster. The best performance comes when all of the data required to satisfy a transaction is held within a single partition so that it can be satisfied within  a single data node rather than being bounced back and forth between multiple nodes where  extra latency will be introduced.

By default, Cluster partions the data by hashing the primary key. This is

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Batching – improving MySQL Cluster performance when using the NDB API
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As many people are aware, the best performance can be achieved from MySQL Cluster by using the native (C++) NDB API (rather than using SQL via a MySQL Server). What’s less well known is that you can improve the performance of your NDB-API enabled application even further by ‘batching’. This article attempts to explain why batching helps and how to do it.

What is batching and why does it help?

NDB API accessing data from the Cluster without batching

Batching involves sending multiple operations from the application to the Cluster in one group rather than individually; the Cluster then processes these operations and sends back the results.

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Are Stored Procedures available with MySQL Cluster?
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The answer is yes – kind of.

Stored procedures are implemented in a MySQL Server and can be used regardless of the storage engine being used for a specific table. One inference from this is that they won’t work when accessing the Cluster database directly through the NDB API.

This leads to the question of whether or not that limitation actually restricts what you can achieve. This article gives a brief introduction to stored procedures and looks at how the same results can be achieved using the NDB API.

Stored procedures provide a rudimentary way of implementing functionality within the database (rather than in the application code). They are implemented by the database designer and have the ability to perform computations as well as make changes to the data in the database. A typical use of stored procedures would be to control all

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Showing entries 1 to 9

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