Learn how to use the service registration and discovery services in ZooKeeper to manage microservices when refactoring from an existing monolithic application.
Distributed transaction tracing is a useful way to monitor or evaluate your microservices architecture for performance, particularly when measuring end-to-end requests.
In this article, excerpted from the book Docker in Action, I will show you how to open access to shared memory between containers. Linux provides a few tools for sharing memory between processes running on the same computer. This form of inter-process communication (IPC) performs at memory speeds. It is often used when the latency associated with network or pipe based IPC drags software performance below requirements. The best examples of shared memory based IPC usage is in scientific computing and some popular database technologies like PostgreSQL. Docker creates a unique IPC namespace for each container by default. The Linux IPC namespace partitions shared memory primitives like named shared memory blocks and semaphores, as well as message queues. It is okay if you are not sure what these are. Just know that they are tools used by Linux programs to coordinate processing. The IPC namespace prevents processes in one container from accessing the memory on the host or in other containers. Sharing IPC Primitives Between Containers I’ve created an image named allingeek/ch6_ipc that contains both a producer and consumer. They communicate using shared memory. Listing 1 will help you understand the problem with running these in separate containers. Listing 1: Launch a Communicating Pair of Programs # start a producer docker -d -u nobody --name ch6_ipc_producer \ allingeek/ch6_ipc -producer # start the consumer docker -d -u nobody --name ch6_ipc_consumer \ allingeek/ch6_ipc -consumer Listing 1 starts two containers. The first creates a message queue and starts broadcasting messages on it. The second should pull from the message queue and write the messages to the logs. You can see what each is doing by using the following commands to inspect the logs of each: docker logs ch6_ipc_producer docker logs ch6_ipc_consumer If you executed the commands in Listing 1 something should be wrong. The consumer never sees any messages on the queue. Each process used the same key to identify the shared memory resource but they referred to different memory. The reason is that each container has its own shared memory namespace. If you need to run programs that communicate with shared memory in different containers, then you will need to join their IPC namespaces with the --ipc flag. The --ipc flag has a container mode that will create a new container in the same IPC namespace as another target container. Listing 2: Joining Shared Memory Namespaces # remove the original consumer docker rm -v ch6_ipc_consumer # start a new consumer with a joined IPC namespace docker -d --name ch6_ipc_consumer \ --ipc container:ch6_ipc_producer \ allingeek/ch6_ipc -consumer Listing 2 rebuilds the consumer container and reuses the IPC namespace of the ch6_ipc_producer container. This time the consumer should be able to access the same memory location where the server is writing. You can see this working by using the following commands to inspect the logs of each: docker logs ch6_ipc_producer docker logs ch6_ipc_consumer Remember to cleanup your running containers before moving on: # remember: the v option will clean up volumes, # the f option will kill the container if it is running, # and the rm command takes a list of containers docker rm -vf ch6_ipc_producer ch6_ipc_consumer There are obvious security implications to reusing the shared memory namespaces of containers. But this option is available if you need it. Sharing memory between containers is safer alternative to sharing memory with the host.
In this article I will tell you how Apache Camel can turn a full-stack Linux microcomputer (like Raspberry Pi) into a device collecting the GPS coordinates.
[This article was written by Sveta Smirnova] Like any good, thus lazy, engineer I don’t like to start things manually. Creating directories, configuration files, specify paths, ports via command line is too boring. I wrote already how I survive in case when I need to start MySQL server (here). There is also the MySQL Sandbox which can be used for the same purpose. But what to do if you want to start Percona XtraDB Cluster this way? Fortunately we, at Percona, have engineers who created automation solution for starting PXC. This solution uses Docker. To explore it you need: Clone the pxc-docker repository:git clone https://github.com/percona/pxc-docker Install Docker Compose as described here cd pxc-docker/docker-bld Follow instructions from the README file: a) ./docker-gen.sh 5.6 (docker-gen.sh takes a PXC branch as argument, 5.6 is default, and it looks for it on github.com/percona/percona-xtradb-cluster) b) Optional: docker-compose build (if you see it is not updating with changes). c) docker-compose scale bootstrap=1 members=2 for a 3 node cluster Check which ports assigned to containers: $docker port dockerbld_bootstrap_1 3306 0.0.0.0:32768 $docker port dockerbld_members_1 4567 0.0.0.0:32772 $docker port dockerbld_members_2 4568 0.0.0.0:32776 Now you can connect to MySQL clients as usual: $mysql -h 0.0.0.0 -P 32768 -uroot Welcome to the MySQL monitor. Commands end with ; or g. Your MySQL connection id is 10 Server version: 5.6.21-70.1 MySQL Community Server (GPL), wsrep_25.8.rXXXX Copyright (c) 2009-2015 Percona LLC and/or its affiliates Copyright (c) 2000, 2015, Oracle and/or its affiliates. All rights reserved. Oracle is a registered trademark of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. Type 'help;' or 'h' for help. Type 'c' to clear the current input statement. mysql> 6. To change MySQL options either pass it as a mount at runtime with something like volume: /tmp/my.cnf:/etc/my.cnf in docker-compose.yml or connect to container’s bash (docker exec -i -t container_name /bin/bash), then change my.cnf and run docker restart container_name Notes. If you don’t want to build use ready-to-use images If you don’t want to run Docker Compose as root user add yourself to docker group
Webpack is the best module bundler I’ve ever used. Just this week I used it to reduce the JS footprint of an app from 906KB to 87KB for mobile visitors. An 800KB difference! Webpack‘s core premise is that you can require('./foo') your JavaScripts. That sea of
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