I'm using nexus sonatype-work nexus3. Still it didn't have a cleaning process. So, it has filled. Is there any commands that I can use to find unused images and data? And, how can delete files manually?
No, you should not delete any of the files manually otherwise it lead to issues with retrieving data from your repositories. You should set up some cleanup policies that will maintain the disk space for you - you can learn more about from these resources:
Cleanup policies
Keeping disk usage low
Storage management
This question is like " What was first, chicken or egg?".
Let's imagine we have some source code. Written using symfony or yii. It has db migration code that hadle some database changes.
Now, we have some commits that updates our code (for example new classes) and some db changes (change old columns or add new tables).
When we developing at localhost or update our dev servers it's ok to have time to stop services\any actions and update server. But when we tries to do it on production server we will crash everything for a while and this is not an option.
Why this will happen - when we pull it (git\mercurial) our code will be updated, but NOT database, and when code will be executed - it will throw exceptions of database. To fix it we should run build-in framework migrations. So in the end our server will be crashed until migrations will be called.
Code and migrations should be updated "in one time".
What is the best practice to handle it?
ADDED:
Solution like "run pull then run migrations in one call" - not an option in highload project. Because on highload even in second some entries\calls can be borken.
Stop server we cannot too.
Pulling off a zero downtime deployment can be a bit tricky and there are many ways to achieve this.
As for the database it is recommended to do changes in a backwards compatible fashion. So for example adding a nullable column or new table will not affect your existing code base and can be done safely. So if you want to add a new non-nullable column you would do it in 3 steps:
Add new column as nullable
Populate with data to make sure there are no null-values
Make the column NOT NULL
You will need a new deployment for 1 & 3 at the very least. When modifying a column it's pretty much the same, you create a new column, transfer the data over, release the code that uses the new column (optionally with the old column as fallback) and then remove the old column (plus fallback code) in a 3rd deployment.
This way you make sure that your database changes will not cause a downtime in your existing application. This takes great care and obviously requires you to have a good deployment pipeline allowing for fast releases. If it takes hours to get a release out this method will not be fun.
You could copy the database (or even the whole system), do a migration and then switch to that instance, but in most applications this is not feasible because it will make it a pain to keep both instances in sync between deployments. I cannot recommend investing too much time in that, but I might be biased from my experience.
When it comes to switching the current version of your code with a newer one you have multiple options. The fancy cloud based solutions like kubernetes make this kind of easy. You create a second cluster with your new version and then slowly route traffic from the old cluster to the new one. If you have a single server it is quite common to deploy a new release to a separate folder, do all the management tasks like warming caches and then when the release is ready to be used you switch a symlink to the newest release. Both methods require meticulous planning and tweaking if you really want them to be zero downtime. There are all kinds of thing that can cause issues like a shared cache being accidentally cleared to sessions not being transferred over correctly to the new release. Whenever something that's stored in a session changes you have to take a similar approach as with the database and basically slow move the state over to the new one while running the code or having a fallback to still handle the old data otherwise you might get errors when reading the session, causing 500 pages for your customers.
They key to deploy with as few outages and glitches as possible is good monitoring of the systems and the application to see where things go wrong during a deployment to make it more stable over time.
You can create a backup server with content that mirrors your current server. Then do some error detection.
If an error is detected on your primary server, update your DNS record to divert your traffic to your secondary server.
Once primary back up and running, traffic moves back to primary and then sync the changes in your secondary.
These are called failover servers.
The blobs folder on my Sonatype Nexus has completely filled the server memory.
Does anyone know how to make room? Does it exist an automatic way to free that space, or I have to do it manually..?
And, at last: what happens if I completety remove all the data in the directory blobs/default/content?
Thank you all in advance
Marco
In NXRM3, the blobstore contains all the components of your repository manager. If your disk is full, you will not be able to write anything more to NXRM and risk corruption of existing data.
Cleanup can be performed using scheduled tasks. What you need varies based around what formats your system is using. You can find more general information here: https://help.sonatype.com/display/NXRM3/System+Configuration#SystemConfiguration-ConfiguringandExecutingTasks
It is important to note that you must run the "Compact blob store" task after any cleanup is done, otherwise the space will not be freed.
However, it is advisable if you have reached full disk space, you shut down and restore from a backup in case there is corruption, preferably giving yourself a larger disk for your blobstore before restarting.
RE "what happens if I completety remove all the data in the directory blobs/default/content": That is in effect removing all data from NXRM in the default blobstore. You will have no components if you do that.
I have an Artifactory instance running an old version of the community software(not pro) that is about to tear down. We experienced lots of index corruption and crashes recently and I'm very concerned about a fatal crash. There's is a lot of artifacts in it and some very precious ones for our business.
I wish to import artifacts from that old instance to a fresh install running the latest version.
I want to know:
what is the best path?
is the migration safe, could it trigger instability on the old Arti?
Is there a way to do a copy/sync will the old Artifactory remains usable?
is it possible to filter out artifacts that I don't want to copy?
You need to export the artifacts (and, probably, the configuration as well) from the old instance and import them into a new one.
If you have consistency issues, that might affect the export.
I would suggest backing up Artifactory (by creating system export), running consistency fix and reindexing and then export again, upgrade and import.
Good news - on the new version you never will have to reindex or run consistency fix :)
The process of creating a new build and releasing it to production is a critical step in the SDLC but it is often left as an afterthought and varies greatly from one company to the next.
I'm hoping people will share improvements they have made to this process in their organisation so we can all takes steps to 'reduce the pain'.
So the question is, specify one painful/time consuming part of your release process and what did you do to improve it?
My example: at a previous employer all developers made database changes on one common development database. Then when it came to release time, we used Redgate's SQL Compare to generate a huge script from the differences between the Dev and QA databases.
This works reasonably well but the problems with this approach are:-
ALL changes in the Dev database are included, some of which may still be 'works in progress'.
Sometimes developers made conflicting changes (that were not noticed until the release was in production)
It was a time consuming and manual process to create and validate the script (by validate I mean, try to weed out issues like problem 1 and 2).
When there were problems with the script (eg the order in which things were run such as creating a record which relies on a foreign key record which is in the script but not yet run) it took time to 'tweak' it so it ran smoothly.
It's not an ideal scenario for Continuous Integration.
So the solution was:-
Enforce a policy of all changes to the database must be scripted.
A naming convention was important for ensuring the correct running order of the scripts.
Create/Use a tool to run the scripts at release time.
Developers had their own copy of the database do develop against (so there was no more 'stepping on each others toes')
The next release after we started this process was much faster with fewer problems, indeed the only problems found were due to people 'breaking the rules', eg not creating a script.
Once the issues with releasing to QA were fixed, when it came time to release to production it was very smooth.
We applied a few other changes (like introducing CI) but this was the most significant, overall we reduced release time from around 3 hours down to a max of 10-15 minutes.
We've done a few things over the past year or so to improve our build process.
Fully automated and complete build. We've always had a nightly "build" but we found that there are different definitions for what constitutes a build. Some would consider it compiling, usually people include unit tests, and sometimes other things. We clarified internally that our automated build literally does everything required to go from source control to what we deliver to the customer. The more we automated various parts, the better the process is and less we have to do manually when it's time to release (and less worries about forgetting something). For example, our build version stamps everything with svn revision number, compiles the various application parts done in a few different languages, runs unit tests, copies the compile outputs to appropriate directories for creating our installer, creates the actual installer, copies the installer to our test network, runs the installer on the test machines, and verifies the new version was properly installed.
Delay between code complete and release. Over time we've gradually increased the amount of delay between when we finish coding for a particular release and when that release gets to customers. This provides more dedicated time for testers to test a product that isn't changing much and produces more stable production releases. Source control branch/merge is very important here so the dev team can work on the next version while testers are still working on the last release.
Branch owner. Once we've branched our code to create a release branch and then continued working on trunk for the following release, we assign a single rotating release branch owner that is responsible for verifying all fixes applied to the branch. Every single check-in, regardless of size, must be reviewed by two devs.
We were already using TeamCity (an excellent continuous integration tool) to do our builds, which included unit tests. There were three big improvements were mentioning:
1) Install kit and one-click UAT deployments
We packaged our app as an install kit using NSIS (not an MSI, which was so much more complicated and unnecessary for our needs). This install kit did everything necessary, like stop IIS, copy the files, put configuration files in the right places, restart IIS, etc. We then created a TeamCity build configuration which ran that install kit remotely on the test server using psexec.
This allowed our testers to do UAT deployments themselves, as long as they didn't contain database changes - but those were much rarer than code changes.
Production deployments were, of course, more involved and we couldn't automate them this much, but we still used the same install kit, which helped to ensure consistency between UAT and production. If anything was missing or not copied to the right place it was usually picked up in UAT.
2) Automating database deployments
Deploying database changes was a big problem as well. We were already scripting all DB changes, but there were still problems in knowing which scripts were already run and which still needed to be run and in what order. We looked at several tools for this, but ended up rolling our own.
DB scripts were organised in a directory structure by the release number. In addition to the scripts developers were required to add the filename of a script to a text file, one filename per line, which specified the correct order. We wrote a command-line tool which processed this file and executed the scripts against a given DB. It also recorded which scripts it had run (and when) in a special table in the DB and next time it did not run those again. This means that a developer could simply add a DB script, add its name to the text file and run the tool against the UAT DB without running around asking others what scripts they last ran. We used the same tool in production, but of course it was only run once per release.
The extra step that really made this work well is running the DB deployment as part of the build. Our unit tests ran against a real DB (a very small one, with minimal data). The build script would restore a backup of the DB from the previous release and then run all the scripts for the current release and take a new backup. (In practice it was a little more complicated, because we also had patch releases and the backup was only done for full releases, but the tool was smart enough to handle that.) This ensured that the DB scripts were tested together at every build and if developers made conflicting schema changes it would be picked up quickly.
The only manual steps were at release time: we incremented the release number on the build server and copied the "current DB" backup to make it the "last release" backup. Apart from that we no longer had to worry about the DB used by the build. The UAT database still occasionally had to be restored from backup (eg. since the system couldn't undo the changes for a deleted DB script), but that was fairly rare.
3) Branching for a release
It sounds basic and almost not worth mentioning, yet we weren't doing this to begin with. Merging back changes can certainly be a pain, but not as much of a pain as having a single codebase for today's release and next month's! We also got the person who made the most changes on the release branches to do the merge, which served to remind everyone to keep their release branch commits to an absolute minimum.
Automate your release process whereever possible.
As others have hinted, use different levels of build "depth". For instance a developer build could make all binaries for runnning your product on the dev machine, directly from the repository while an installer build could assemble everything for installation on a new machine.
This could include
binaries,
JAR/WAR archives,
default configuration files,
database scheme installation scripts,
database migration scripts,
OS configuration scripts,
man/hlp pages,
HTML documentation,
PDF documentation
and so on. The installer build can stuff all this into an installable package (InstallShield, ZIP, RPM or whatever) and even build the CD ISOs for physical distribution.
The output of the installer build is what is typically handed over to the test department. Whatever is not included in the installation package (patch on top of the installation...) is a bug. Challenge your devs to deliver a fault free installation procedure.
Automated single step build. The ant build script edits all the installer configuration files, program files that need changed ( versioning) and then builds. No intervention required.
There is still a script run to generate the installers when it's done, but we will eliminate that.
The CD artwork is versioned manually; that needs fixed too.
Agree with previous comments.
Here is what has evolved where I work. This current process has eliminated the 'gotchas' that you've described in your question.
We use ant to pull code from svn (by tag version) and pull in dependencies and build the project (and at times, also to deploy).
Same ant script (passing params) is used for each env (dev, integration, test, prod).
Project process
Capturing requirements as user 'stories' (helps avoid quibbling over an interpretation of a requirement, when phrased as a meaningful user interaction with the product)
following an Agile principles so that each iteration of the project (2 wks) results in demo of current functionality and a releasable, if limited, product
manage release stories throughout the project to understand what is in and out of scope (and prevent confusion abut last minute fixes)
(repeat of previous response) Code freeze, then only test (no added features)
Dev process
unit tests
code checkins
scheduled automated builds (cruise control, for example)
complete a build/deploy to an integration environment, and runs smoke test
tag the code and communicate to team (for testing and release planning)
Test process
functional testing (selenium, for example)
executing test plans and functional scenarios
One person manages the release process, and ensures everyone complies. Additionally all releases are reviewed a week before launch. Releases are only approved if there are:
Release Process
Approve release for a specific date/time
Review release/rollback plan
run ant with 'production deployment' parameter
execute DB tasks (if any) (also, these scripts can be version and tagged for production)
execute other system changes / configs
communicate changes
I don't know or practice SDLC, but for me, these tools have been indispensible in achieving smooth releases:
Maven for build, with Nexus local repository manager
Hudson for continuous integration, release builds, SCM tagging and build promotion
Sonar for quality metrics.
Tracking database changes to development db schema and managing updates to qa and release via DbMaintain and LiquiBase
On a project where I work we were using Doctrine's (PHP ORM) migrations to upgrade and downgrade the database. We had all manner of problems as the generated models no longer matched with the database schema causing the migrations to completely fail half way.
In the end we decided to write our own super basic version of the same thing - nothing fancy, just up's and down's that execute SQL. Anyway it worked out great (so far - touch wood). Although we were reinventing the wheel slightly by writing our own, the fact that the focus was on keeping it simple meant that we have far less problems. Now a release is a cinch.
I guess the moral of the story here is that it is sometimes OK to reinvent the wheel some times as long as you are doing so for a good reason.