It doesn’t exist until it is nicely documented. Now Aspersa has documentation. Writing these tools has taught me how powerful and flexible Bash can be. Solving MySQL problems is a lot easier with good tools!
Related posts:
While working on a deployment we came across a nasty surprise. In
hindsight it was avoidable, but it never crossed our minds it
could happen. I'll share the experience so when you face a
similar situation, you'll know what to expect.
ScenarioTo deploy the changes, we used a pair of servers
configured to replicate with each other (master-master
replication). There are many articles that describe how to
perform an ALTER TABLE with minimum or no downtime using MySQL
replication. The simple explanation is:
The details to make this work will depend on each individual situation and are too extensive for the purpose of this article. A …
[Read more]Sqlexamples.org is a community project that is focused on collecting real world solutions for specific problems. Additionally, we want to collect database know-how that is related to SQL or NoSQL databases of all kinds. Content is indexed and freely available to everybody. We would like to invite every single database developer and administrator out there to take part! It does not matter which database you prefer, Oracle, MS SQL, MySQL, PostgreSQL, SQLite, CouchDB or MongoDB...
I recently started looking over how to get information about
relay log file and binary log file using an SQL interface. Being
able to do that can be quite handy when one is going to work with
replication in various ways. In my particular case, I wanted to
get the path to the relay log index file and binary log index
file to be able to read the binary log files as well as the relay
log files directly. You are probably familiar with the
--relay-log-index and --relay-log
options that can be set to specify where the index file. These
options can either be used to set an absolute path or a relative
path to use for the files. If the option starts with a
/, it is considered an absolute path (drive letters
are allowed on Windows though), otherwise the path is relative to
the data directory (which is specified through the
--datadir option). The values supplied to these
options are provided from SQL as the …
I was interviewed by Justin Kestelyn the OTN Senior Director about MySQL at Oracle Open World this week.
Some highlights of the questions asked:
You can view the Video online or play below.
In the last 9 months, every time I visited a customer, or I participated to a meetup group or I simply had a chat with a non-MySQL colleague, I heard the very same question: “when should I use MySQL and when should I use Oracle?”. The most audacious dared to ask how difficult it is to migrate from one database to another. Some more conservative DBAs and developers asked me if and how it is possible to integrate both database servers in the same environment or solution.
As you can imagine, there are not simple answers to these questions. There are certainly some guidelines that may help in making a decision around the use of MySQL, Oracle, or any other RDBMS.
When should I use Oracle 11g and when should I use MySQL Enterprise?
The ultra-simplistic answer can be summarised in:
We all know that it’s good practice for queries to use indexes but it’s not that clear-cut, there are good indexes and poor indexes. By making indexes more efficient you stand to increase query speeds and the places that MySQL can use the indexes effectively.
Column Size
The size of the column or columns you’re indexing is relevant because the less data the database server has to search through or index the faster it’ll be and the less storage you’ll use on disk. In this first post we’ll just be concentrating on column size optimizations.
There’s a full list of all data types and how much storage they require in the MySQL documentation: Data Type Storage Requirements
Common places where the wrong data type could be specified include:
Numeric types
This is probably the biggest offender …
[Read more]When we first started selling O'Reilly ebooks on Kindle, the limitations of the device prevented us from including our full catalog of ebooks. The first generation Kindle wasn't able to display tables or computer code -- two classes of content that are obviously quite common in O'Reilly books.
Although we (and our authors!) had hoped that Amazon would update the software on those Kindles (especially since many O'Reilly readers are also typically early adopters of new devices and technologies), it's clear that Amazon is focusing their efforts on their newer devices and apps for other mobile devices like iPad and Android.
Amazon won't disclose any data about how many customers use specific Kindle devices or apps, but we believe the number of Kindle 1 users is a small and shrinking part of the overall Kindle user base, and since there are multiple additional apps and desktop readers from Amazon, with a few specific exceptions …
[Read more]
There are times when you may be rebuilding a DB server by
replaying the binary logs using the mysqlbinlog
utility. Extracting CRUD statements and DDL is relatively
straightforward, but not for statements like LOAD DATA
INFILE. The actual data file is embedded within the binary
log, and not very visible to the naked eye. But there is an easy
way to decipher the binary log and extract the file to load
manually.
As an example, I have taken a simple text file of numbers and
loaded it into a fictitious table abc using the LOAD DATA
LOCAL INFILE command. To see where in the binary log that
command would reside, the mysqlbinlog utility is
used:
[Read more]
$ mysqlbinlog mysqld-bin.000003 | grep -i -B7 "load data"
# at 174
#100921 21:42:10 server id 1136902037 end_log_pos 218
#Begin_load_query: file_id: 1 block_len: 21
# at 218
#100921 21:42:10 …
SMAQ report sections
"Big data" is data that becomes large enough that it cannot be processed using conventional methods. Creators of web search engines were among the first to confront this problem. Today, social networks, mobile phones, sensors and science contribute to petabytes of data created daily.
To meet the challenge of processing such large data sets, Google created MapReduce. Google's work and Yahoo's creation of the Hadoop MapReduce implementation has spawned an ecosystem of big data processing tools.
As MapReduce has grown in popularity, a stack for big data systems …
[Read more]