Persistent Data in a MySQL Docker Container

Running MySQL in Docker

In a recent article on Docker in this blog, we presented some basics for dealing with data in containers. This article will present another popular application for Docker: MySQL containers. Running MySQL instances in Docker allows isolating database infrastructure with ease.

Connecting to the Standard MySQL Container

The description of the MySQL docker image provides a lot of useful information how to launch and connect to a MySQL container. The first step is to create standard MySQL container from the latest available image.

sudo docker run \
   -p 3307:3306 

This creates a MySQL container where the root password is set to secret. As the host is already running its own MySQL instance (which has nothing to do with this docker example), the standard port 3306 is already taken. Thus we publish utilise the port 3307 on the host system and forward it to the 3306 standard port from the container.

Connect from the Host

We can then connect from the command line like this:

mysql -uroot -psecret -h -P3307

We could also provide the hostname localhost for connecting to the container, but as the MySQL client per default assumes that a localhost connection is via a socket, this would not work. Thus when using the hostname localhost, we needed to specify the protocol TCP, wo that the client connects via the network interface.

mysql -uroot -psecret -h localhost --protocol TCP -P3307

Connect from other Containers

Connecting from a different container to the MySQL container is pretty straight forward. Docker allows to link two containers and then use the exposed ports between them. The following command creates a new ubuntu container and links to the MySQL container.

sudo docker run -it --name ubuntu-container --link mysql-instance:mysql-link ubuntu:16.10 bash

After this command, you are in the terminal of the Ubuntu container. We then need to install the MySQL client for testing:

# Fetch the package list
root@7a44b3e7b088:/# apt-get update
# Install the client
root@7a44b3e7b088:/# apt-get install mysql-client
# Show environment variables
root@7a44b3e7b088:/# env

The last command gives you a list of environment variables, among which is the IP address and port of the MySQL container.


You can then connect either manually of by providing the variables

mysql -uroot -psecret -h

If you only require a MySQL client inside a container, simply use the MySQL image from docker. Batteries included!

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Plotting Colourful Graphs with R, RStudio and Ggplot2

The Aesthetics of Data Science

Data visualization is a powerful tool for communicating results and recently receives more and more attention due to the hype of data science. Integrating a meaningful graph into a paper or your thesis could improve readability and understandability more than any formulas or extended textual descriptions can. There exists a variety of different approaches for visualising data. Recently a lot of new Javascript based frameworks have gained quite some momentum, which can be used in Web applications and apps. A more classical work horse for data science is the R project and its plotting engine ggplot2. The reason why I decide to stick with R is its popularity and flexibility, which is still  impressive. Also with RStudio, there exists a convenient IDE which provides useful features for data scientists.

Plotting Graphs

In this blog post, I demonstrate how to plot time series data and use colours to highlight a specific aspect of data. As almost all techniques, R and ggplot2 require practise and training, which I realised again today when I spent quite a bit of time struggling with getting a simple plot right.

Currently I am evaluating two systems I developed and I needed to visualize their storage and execution time demands in comparison. My goal was to create a plot for each non-functional property, the execution time and the storage demand, while each plot should depict both systems’ performance. Each system runs a set of operations, think of create, read, update and delete operations (CRUD). Now for visualizing which of these operations has the most effects on the system, I needed to colourise each operation within one graph. This is the easy part. What was more tricky is to provide for each graph a defined set of colours, which can be mapped to each instance of the variable. Things which have the same meaning in both graphs should visualized in the same way, which requires a little hack.


Install the following packages via apt

sudo apt-get install r-base r-recommended r-cran-ggplot2

and RStudio by downloading the deb – File from the project homepage.

Evaluation Data

As an example,we plan to evaluate the storage demand of two different systems and compare the results. Consider the following sample data.

# Set seed to get the same random numbers for this example
# Generate 200 random data records
N <- 200
# Generate a random, increasing sequence of integers that we assume is the storage demand in some unit
storage1 =sort(sample(1:100000, size = N, replace = TRUE),decreasing = FALSE)
storage2 = sort(sample(1:100000, size = N, replace = TRUE),decreasing = FALSE)
# Define the operations availabel and draw a random sample
operationTypes = c('CREATE','READ','UPDATE','DELETE')
operations = sample(operationTypes,N,replace=TRUE)
# Create the dataframe
df  df
     id storage1 storage2 operations
1     1       24      238     CREATE
2     2      139     1755     UPDATE
3     3      158     1869     UPDATE
4     4      228     2146       READ
5     5      395     2967     DELETE
6     6      734     3252     CREATE
7     7      789     4049     DELETE
8     8     2909     4109       READ
9     9     3744     4835     CREATE
10   10     3894     4990       READ


We created a random data set simulating the characteristics of system measurement data. As you can see, we have a list of operations of the four types CREATE, READ, UPDATE and DELETE and a measurement value for the storage demand in both systems.

The Simple Plot

Plotting two graphs of thecolumns storage1 and storage2 is straight forward.

# Simple plot
p1 <- ggplot(df, aes(x,y)) +
  geom_point(aes(x=id,y=storage1,color="Storage 1")) +
  geom_point(aes(x=id,y=storage2,color="Storage 2")) +
  ggtitle("Overview of Measurements") +
  xlab("Number of Operations") +
  ylab("Storage Demand in MB") +
  scale_color_manual(values=c("Storage 1"="forestgreen", "Storage 2"="aquamarine"), 
                     name="Measurements", labels=c("System 1", "System 2"))


We assign for each point plot a color. Note that the color nme "Storage 1" for instance of course does not denote a color, but it assignes a level for all points of the graph. This level can be thought of as a category, which ensures that all the points which belong to the same category have the same color. As you can see at the definition of the color scale, we assign the actual color to this level there.  This is the result:

plot1Plotting Levels

A common task is to visualise categories or levels of measurement data. In this example, there are four different levels we could observe: CREATE, READ, UPDATE and DELETE.

# Plot with levels
p1 <- ggplot(df, aes(x,y)) +
  geom_point(aes(x=id,y=storage1,color=operations)) +
  geom_point(aes(x=id,y=storage2,color=operations)) +
  ggtitle("Overview of Measurements") +
  labs(color="Measurements") +

Instead of assigning two colours, one for each graph, we can also assign colours to the operations. As you can see in the definition of the graphs and the colour scale, we map the colours to the variable operations instead. As a result we get differently coloured points per operation, but we get these of course for both graphs in an identical fashion as the categories are the same for both measurements. The result looks like this:

plot2Now this is obviously not what we want to achieve as we cannot differentiate between the two graphs any more.

Plotting the same Levels for both Graphs in Different Colours

This last part is a bit tricky, as ggplot2 does not allow assigning different colour schemes within one plot. There do exist some hacks for this, but the solution does not improve the readability of the code in my opinion. In order to apply different colour schemes for the two graphs while still using the categories, I appended two extra columns to the data set. If we append some differentiation between the two graphs and basically double the categories from four to eight, where each graph now uses its own four categories, we can also assign distinct colours to them.

df$operationsStorage1 <- paste(df$operations,"-Storage1", sep = '')
df$operationsStorage2 <- paste(df$operations,"-Storage2", sep = '')

p3 <- ggplot(df, aes(x,y)) +
  geom_point(aes(x=id,y=storage1,color=operationsStorage1)) +
  geom_point(aes(x=id,y=storage2,color=operationsStorage2)) +
  ggtitle("Overview of Measurements") +
  xlab("Number of Operations") +
  ylab("Storage Demand in MB") +
  labs(color="Operations") +

We then assign the new column for each system individually as colour value. This ensures that each graph only considers the categories that we assigned in this step. Thus we can assign a different color scheme for wach graph and print the corresponding colours in the label (legend) next to the chart. This is the result:


Now we can see which operation was used at every measurement and still be able to distinguish between the two systems.

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Neue Rezension: Linux Magazin – Alles über MySQL

Das Linux Magazin hat in seiner Ausgabe 09/2015 die folgende Rezension veröffentlicht:


Dass dieses Buch wenige Fragen offen lassen will, machen schon seine Dimensionen deutlich: Über 800 Seiten ergeben einen soliden Wälzer. So stellen die Autoren ihren umfangreichen Erklärungen zu MySQL denn auch eine Einführung in das Thema Datenbanken an sich voran und machen den Leser mit den grundlegenden Techniken der Datenmodellierung bekannt.

Im zweiten Teil geht es dann systematisch um die MySQL-Administration: Er erläutert die Architektur der Datenbank, die Installation und die Bordwerkzeuge nebst einem Abstecher zu den Themen Backup und Monitoring sowie die Performance-Optimierung im Hinblick auf Hardware und Konfiguration. Der Optimierung von Abfragen ist dagegen ein eigenes Kapitel gewidmet. Außerdem werden in eigenen Kapiteln Replikation und Hochverfügbarkeit beziehungsweise Sicherheit diskutiert.

Der sehr umfangreiche dritte Teil widmet sich dann der Software-Entwicklung in und für MySQL: Angefangen von Stored Procedures über SQL-Programmierung bis zu Schnittstellen zu anderen Programmiersprachen. Auch NoSQL mit MySQL stellt er von Grund auf vor.

Die Autoren verstehen es, ihren Stoff gründlich und gut verständlich zu vermitteln. Zahlreiche Beispiele illustrieren das Dargebotene. Damit ist das Buch eine Empfehlung für angehende DBAs, aber auch für den gestandenen Admin, dem es als Referenz nützlich ist.

Quelle: Linux Magazin

Mehr Informationen zur aktuellen und früheren Auflagen des umfassenden Handbuchs finden Sie hier.

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Die dritte Auflage des MySQL Handbuchs

Nachdem wir im Dezember erfahren hatten, dass sich die Bestände der zweiten Auflage zu Ende neigen, ging es für uns Autoren an die Planung der dritten, aktualisierten Auflage des MySQL Buchs. Von Jänner bis April 2015 haben Eva Zangerle, Wolfgang Gassler und ich an der Neuauflage unseres Buches gearbeitet. Eva, Wolfgang und ich haben seitenweise Release-Notes durchgeackert, Leser-feedback und wünsche eingearbeitet und natürlich so gut wie alle neuen Features berücksichtigt. Selbstverständlich haben wir auch das Datenset komplett neu erzeugt und mit Millionen von Einträgen gefüttert. Wir haben alle Beispiele selbst noch ein mal durch-getestet, damit uns auch ja nichts entgeht und haben den ein oder anderen Schnitzer ausgebessert.


Diese Auflage ist nun bereits unter dem neuen Verlagsnamen Rheinwerk Verlag erscheinen und seit 25. Mai verfügbar. Sie erhalten das Buch im gut sortierten Buchhandel, direkt beim Rheinwerk Verlag (versandkostenfrei) und natürlich auch bei den Großen. Details zur dritten Auflage und dem Prozess, der hinter einer Neuauflage steht, finden Sie hier.

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