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Displaying posts with tag: MySQL Cluster (reset)
Automatic Schema Synchronization in NDB Cluster 8.0: Performance Schema Tables

The Automatic Schema Synchronization mechanism, detailed in a two part series: part 1 and part 2, automatically detects and synchronizes mismatches in metadata between NDB Dictionary and MySQL Server’s Data Dictionary. Information about the mechanism is exposed through different MySQL Server status variables.…

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Install MySQL Cluster Manager "MCM" using Systemd

For more information on how to create a service look at man systemd.service Install Directory for MCM installation is /opt Download latest tar package of MCM from or (today this is package: MySQL Cluster Manager 1.4.8 TAR for Generic Linux x86 (64bit)) Run commands below once you have downloaded the MCM package:

sudo su -
cd /opt
tar xzf /tmp/mcm-1.4.8-linux-glibc2.12-x86-64bit.tar.gz
mv mcm-1.4.8-linux-glibc2.12-x86-64bit mcm
cp /opt/mcm/mcm1.4.8/etc/mcmd.ini /opt/mcm/
mkdir /opt/mcm/mcm_data

Set manager-directory to folder /opt/mcm/mcm_data in configuration file:

cat /opt/mcm/mcmd.ini | sed 's/^##manager-directory.*/manager-directory = \/opt\/mcm\/mcm_data/' >> /opt/mcm/mcmd.ini

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Use Case: Continuous MySQL Operations for Growing SaaS Business

In this fourth post in our MySQL Use Case Blog Series we look at a customer of ours who was able to grow their Saas business from tens of customers initially to thousands of enterprise customers once they achieved continuous MySQL operations with Continuent Tungsten.

This particular customer, based in California, develops and sells automation software for account-based marketing as well as other marketing services and products such as SEO and content marketing. They provide solutions tailored for large enterprises and fast-growing, small businesses alike covering all industry types from technology all the way to higher education.

So how did this fast growing marketing automation SaaS provider scale from tens of customers to thousands of enterprise customers using Tungsten Clustering (currently with 600+ MySQL instances)?

The Challenge

SaaS and other web applications are inherently 24/7/365 operations, thus they …

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NDB Cluster, the World's Fastest Key-Value Store

Using numbers produced already with MySQL Cluster 7.6.10 we have
shown that NDB Cluster is the world's fastest Key-Value store using
the Yahoo Cloud Serving Benchmark (YCSB) Workload A.

Presentation at

We reached 1.4M operations using 2 Data Nodes and 2.8M operations
using a 4 Data Node setup. All this using a standard JDBC driver.
Obviously using a specialised ClusterJ client will improve performance
further. These benchmarks was executed by Bernd Ocklin.

The benchmark was executed in the Oracle Cloud. Each Data Node used
a Bare Metal Server using DenseIO which have 52 CPU cores with
8 NVMe drives.

The MySQL Servers and Benchmark clients was executed on Bare Metal
servers with 2 MySQL Server per server (1 MySQL Server per …

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Automatic Schema Synchronization in NDB Cluster 8.0: Part 2

In part 1, we took a brief, high-level look at the various protocols and mechanisms used to keep the Data Dictionary (DD) of MySQL servers connected to a MySQL Cluster in synchronization with each other and with the NDB Dictionary.…

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Faster restarts with local and partial checkpoints in MySQL NDB Cluster

The MySQL NDB Cluster team works on fundamental redesigns of core parts of NDB architecture. One of these changes is the partial checkpoint algorithm. You can now take full advantage of it when building much larger clusters: NDB 8.0 can use 16 TB data memory per data node for in-memory tables.

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Automatic Schema Synchronization in NDB Cluster 8.0: Part 1

Data nodes are the distributed, sharded storage core of MySQL NDB Cluster. Its data is usually accessed by MySQL Servers (also called SQL nodes in NDB parlance). The MySQL servers each have their own transactional Data Dictionary (DD) where all the metadata describing tables, databases, tablespaces, logfile groups, foreign keys, and other objects are stored for use by MySQL server.…

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Support 3-4 replicas in NDB Cluster 8.0

One thing that we announced in the GA release of MySQL Cluster 8.0 is
that we now support using 3 and 4 replicas. Actually the NDB software
was designed to support 1 through 4 replicas from the very get go in the
1990s. The reason it has not been supported is simply that we haven't
had any regular testing effort attached to it. Thus we haven't felt that
we can support such an important feature without having proper testing
of it.

What we did in the development of MySQL Cluster 8.0 is that we have
added a number of new configurations with both 3 replicas and with
4 replicas. These configurations will now be used in our daily testing
of NDB.

In the process of working on this we found some issues with arbitration
already in MySQL Cluster 7.6. This issue was fixed in 7.6 already.
So the way to decide how to handle a node failure in 7.6 and …

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NDB Parallel Query, part 5

In this part we are going to analyze a bit more complex query than before.
This query is a 6-way join.

The query is:
        SUM(volume) AS revenue
                        n1.n_name AS supp_nation,
                        n2.n_name AS cust_nation,
                        extract(year FROM l_shipdate) as l_year,
                        l_extendedprice * (1 - l_discount) AS …

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NDB Parallel Query, part 4

In this part we will discuss how NDB batch handling works. Query execution of
complex SQL queries means that more rows can be delivered than the receiver is
capable of receiving. This means that we need to create a data flow from the
producer where the data resides and the query executor in the MySQL Server.

The MySQL Server uses a record where the storage engine have to copy the result
row into the record. This means that the storage of batches of rows is taken
care of by the storage engine.

When NDB performs a range scan it will decide on the possible parallelism before
the scan is started. The NDB API have to allocate enough memory to ensure that
we have memory prepared to receive the rows as they arrive in a flow of result
rows from the data nodes. It is possible to set batch size of hundreds and even
thousands of rows for a query.

The …

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