Usually, lightweight images are tagged :alpine or :slim. This significant difference can make your builds faster. Image node-alpine, however, is 9x smaller. If you’re developing a node.js-based application, you’re probably using an official node image (FROM node). So, the size of your final image will depend not only on how much you put inside, but also which base image you use. If you use a small base image, however, you’ll get a very small container. So, if you use a large base image, you’ll end up with a big container. Everything you want to have in your container will be added “on top” of the base Docker image. Every Dockerfile (which is a definition of your container) starts with the keyword “FROM.” This instruction tells Docker which base image to use to build your container. Let me briefly explain how Docker containers work to better understand this. The first, and the easiest, way to improve your container build and startup time is to use a slim Docker base image. Therefore, it’s important to have a good monitoring system, which can help you find where your performance is lacking. So, if you suffer from long application startup or restarts, it can be either Docker or the application itself that loads for a long time. Most of the performance improvements to Docker improve container build and startup time only. ![]() You should keep in mind, however, that the performance of your application running inside the container isn’t really influenced by Docker itself. Other, more advanced options require a bit more effort-they can’t be applied to every setup, but they bring even more advantages. They don’t typically require many changes to your containers, so it’s an easy win. But if you’re looking for maximum performance, where every second counts, you’ll need to learn how to improve Docker performance even more.Ī few general and easy-to-implement performance tips can be applied pretty much universally. Containers usually start in a few seconds and deploying newer versions of a container doesn’t take much longer. This is due to the way containers work-they’re really fast. Server Version: 17.09.Using Docker containers is one of the most popular ways to build modern software these days. Importing a gigabyte of data from an external server takes half an hour on localhost, but close to 24 hours on this container. The volume driver is local and on my system the mounts show up as nsfs. I’m running mysql:5.7 on Docker version 17.09.0-ce, build afdb6d4 with a local volume for the data directory, which is ext4. ![]() Log: awslogs fluentd gcplogs gelf journald json-file logentries splunk syslogĬontainerd version: 6e23458c129b551d5c9871e5174f6b1b7f6d1170 ![]() Network: bridge host macvlan null overlay dev/mapper/data–vg-docker on /var/lib/docker/aufs type xfs docker info dev/mapper/data–vg-docker on /var/lib/docker/plugins type xfs (rw,relatime,attr2,inode64,noquota) dev/mapper/data–vg-vbox on /srv/home/vbox type ext4 (rw,relatime,data=ordered) ![]() dev/mapper/data–vg-vbox on /home/vbox type ext4 (rw,relatime,data=ordered) dev/mapper/data–vg-data on /home type ext4 (rw,relatime,data=ordered) dev/mapper/data–vg-data on /srv type ext4 (rw,relatime,data=ordered) dev/mapper/data–vg-docker on /var/lib/docker type xfs (rw,relatime,attr2,inode64,noquota) dev/sde on /mnt/disk1 type ext4 (rw,relatime,data=ordered) dev/sda1 on /boot type ext2 (rw,relatime,block_validity,barrier,user_xattr,acl) dev/mapper/cloud–vg-root on / type ext4 (rw,relatime,errors=remount-ro,data=ordered) It’s about 15 times slower than the VirtualBox on the same machine since $HOME is on ext4 time mysql dockerdb < drupal3.sql
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