My mfs version is moosefs-ce-2.0, it is installed on debian6 which is ext3 filesystem. There are a master and a metalogger and some chunkserver, when my master is down. How to recover master from metalogger? The documentation moosefs.org provided is outdated, I can't find more detailed information on documentaton. Or how to config muti-master on moosefs-ce-2.0?
It is described in the documentation. You can find the documentation here: MooseFS Documentation. Paragraph 4.2 (page 19) of MooseFS User's Manual "Master metadata restore from metaloggers" says:
4.2 Master metadata restore from metaloggers
In MooseFS Community Edition basic configuration there can be only one master and several metaloggers. If for some reason you loose all metadata files and changelogs from master server you can use data from metalogger to restore your data. To start dealing with recovery first you need to transfer all data stored on metalogger in /var/lib/mfs to master metadata folder. Files on metalogger will have ml prefix prepended to the filenames. After all files are copied, you need to create metadata.mfs file from changelogs and metadata.mfs.back files. To do this we need to use the command mfsmaster -a. Mfsmaster starts to build new metadata file and starts mfsmaster process.
Related
I have an azure blob container with data which I have not uploaded myself. The data is not locally on my computer.
Is it possible to use dvc to download the data to my computer when I haven’t uploaded the data with dvc? Is it possible with dvc import-url?
I have tried using dvc pull, but can only get it to work if I already have the data locally on the computer and have used dvc add and dvc push .
And if I do it that way, then the folders on azure are not human-readable. Is it possible to upload them in a human-readable format?
If it is not possible is there then another way to download data automatically from azure?
I'll build up on #Shcheklein's great answer - specifically on the 'external dependencies' proposal - and focus on your last question, i.e. "another way to download data automatically from Azure".
Assumptions
Let's assume the following:
We're using a DVC pipeline, specified in an existing dvc.yaml file. The first stage in the current pipeline is called prepare.
Our data is stored on some Azure blob storage container, in a folder named dataset/. This folder follows a structure of sub-folders that we'd like to keep intact.
The Azure blob storage container has been configured in our DVC environment as a DVC 'data remote', with name myazure (more info about DVC 'data remotes' here)
High-level idea
One possibility is to start the DVC pipeline by synchronizing a local dataset/ folder with the dataset/ folder on the remote container.
This can be achieved with a command-line tool called azcopy, which is available for Windows, Linux and macOS.
As recommended here, it is a good idea to add azcopy to your account or system path, so that you can call this application from any directory on your system.
The high-level idea is:
Add an initial update_dataset stage to the DVC pipeline that checks if changes have been made in the remote dataset/ directory (i.e., file additions, modifications or removals).
If changes are detected, the update_datset stage shall use the azcopy sync [src] [dst] command to apply the changes on the Azure blob storage container (the [src]) to the local dataset/ folder (the [dst])
Add a dependency between update_dataset and the subsequent DVC pipeline stage prepare, using a 'dummy' file. This file should be added to (a) the outputs of the update_dataset stage; and (b) the dependencies of the prepare stage.
Implementation
This procedure has been tested on Windows 10.
Add a simple update_dataset stage to the DVC pipeline by running:
$ dvc stage add -n update_dataset -d remote://myazure/dataset/ -o .dataset_updated azcopy sync \"https://[account].blob.core.windows.net/[container]/dataset?[sas token]\" \"dataset/\" --delete-destination=\"true\"
Notice how we specify the 'dummy' file .dataset_updated as an output of the stage.
Edit the dvc.yaml file directly to modify the command of the update_dataset stage. After the modifications, the command shall (a) create the .dataset_updated file after the azcopy command - touch .dataset_updated - and (b) pass the current date and time to the .dataset_updated file to guarantee uniqueness between different update events - echo %date%-%time% > .dataset_updated.
stages:
update_dataset:
cmd: azcopy sync "https://[account].blob.core.windows.net/[container]/dataset?[sas token]" "dataset/" --delete-destination="true" && touch .dataset_updated && echo %date%-%time% > .dataset_updated # updated command
deps:
- remote://myazure/dataset/
outs:
- .dataset_updated
...
I recommend editing the dvc.yaml file directly to modify the command, as I wasn't able to come up with a complete dvc add stage command that took care of everything in one go.
This is due to the use of multiple commands chained by &&, special characters in the Azure connection string, and the echo expression that needs to be evaluated dynamically.
To make the prepare stage depend on the .dataset_updated file, edit the dvc.yaml file directly to add the new dependency, e.g.:
stages:
prepare:
cmd: <some command>
deps:
- .dataset_updated # add new dependency here
- ... # all other dependencies
...
Finally, you can test different scenarios on your remote side - e.g., adding, modifying or deleting files - and check what happens when you run the DVC pipeline up till the prepare stage:
$ dvc repro prepare
Notes
The solution presented above is very similar to the example given in DVC's external dependencies documentation.
Instead of the az copy command, it uses azcopy sync.
The advantage of azcopy sync is that it only applies the differences between your local and remote folders, instead of 'blindly' downloading everything from the remote side when differences are detected.
This example relies on a full connection string with an SAS token, but you can probably do without it if you configure azcopy with your credentials or fetch the appropriate values from environment variables
When defining the DVC pipeline stage, I've intentionally left out an output dependency with the local dataset/ folder - i.e. the -o dataset part - as it was causing the azcopy command to fail. I think this is because DVC automatically clears the folders specified as output dependencies when you reproduce a stage.
When defining the azcopy command, I've included the --delete-destination="true" option. This allows synchronization of deleted files, i.e. files are deleted on your local dataset folder if deleted on the Azure container.
Please, bear with me, since you have a lot of questions. Answer needs a bit structure and background to be useful. Or skip to the very end to find some new ways of doing Is it possible to upload them in a human-readable format? :). Anyways, please let me know if that solves your problem, and in general would be great to have a better description of what you are trying to accomplish at the end (high level description).
You are right that by default DVC structures its remote in a content-addressable way (which makes it non human-readable). There are pros and cons to this. It's easy to deduplicate data, it's easy to enforce immutability and make sure that no one can touch it directly and remove something, directory names in projects make it connected to actual project and their meaning, etc.
Some materials on this: Versioning Data and Models, my answer of on how DVC structures its data, upcoming Data Management User Guide section (WIP still).
Saying that, it's clear there are downsides to this approach, especially when it comes to managing a lot of objects in the cloud (e.g. millions of images, etc). To name a few concerns that I see a lot as a pattern:
Data has been created (and being updated) by someone else. There is some ETL, third party tool, etc. We need to keep that format.
Third party tool expect to have data in "human" readable way. It doesn't integrate with DVC to being able to access it indirectly via Git. (one of the examples - Label Studio need direct links to S3).
It's not practical to move all of data into DVC, it doesn't make sense to instantiate all the files at once as one directory. Users need slices, usually based on some annotations (metadata), etc.
So, DVC has multiple features to deal with data in its own original layout:
dvc import-url - it'll download objects, it'll cache them, and will by default push (dvc push) to remote to again save them to guarantee reproducibility (this can be changed). This command creates a special file .dvc that is being used to detect changes in the cloud to see if DVC needs to download something again. It should cover the case for "to download data automatically from azure".
dvc get-url - this more or less wget or rclone or aws s3 cp, etc with multi cloud support. It just downloads objects.
A bit advanced thing (if you DVC pipelines):
Similar to import-url but for DVC pipelines - external dependencies
The the third (new) option. It's in beta phase, it's called "cloud versioning" and essentially it tries to keep the storage human readable while still benefit from using .dvc files in Git if you need them to reference an exact version of the data.
Cloud Versioning with DVC (it's WPI when I write this, if PR is merged it means you can find it in the docs
The document summarizes well the approach:
DVC supports the use of cloud object versioning for cases where users prefer to retain their original filenames and directory hierarchy in remote storage, in exchange for losing the de-duplication and performance benefits of content-addressable storage. When cloud versioning is enabled, DVC will store files in the remote according to their original directory location and
filenames. Different versions of a file will then be stored as separate versions of the corresponding object in cloud storage.
Looking to setup a high performance environment running Mongo 3.4 on windows 2016 in azure. I come from a SQL\windows background and was wondering if there are any options with Mongo to spread out the IO workload of mongod. It seems odd that there is only a dbPath option and that you can not configure separate locations for the DB(s), opslogs and journal. Am i missing something ?
Thanks for any assistance
This is indeed possible, using a couple of different techniques:
The oplog is stored in the local database, so you can keep it in a separate folder by using the storage.directoryPerDB config option.
The journal is stored in a subfolder of the data directory; you can make MongoDB save its journal files in a separate directory by preparing a symbolic link called journal in the data directory, pointing to your other folder.
We recently changed mastership of a stream from one site (inh) to another(ies). Things were fine till following error.
Now a delivery from child branch to the "moved branch" results in error. Not all merges are problematic. Select directories (or I think so) are not merging.
Unable to perform operation "make branch" in replica "interfaces_src_ies" of VOB "\interfaces_src".
Master replica of branch type "project_subset_QPE-5060" is "interfaces_src.inh".
There is no candidate version which can be checked out.
Unable to check out "M:\dyn_project_subset\interfaces_src\src\java\src\chs\cof\project".
How can I fix this? How can I change mastership of "branch type "project_subset_QPE-5060 to interfaces_src.ies
That should mean, as detailed in the IBM technote swg21142784, that the mastership transfer was incomplete.
That can happen when there was a checked out file at the time of the transfer.
Make sure there is no checked out files (on both sites), and try and transfer the mastership again (even if it says it is already transferred)
Or, as described in the technote, try and create the branch on the other site, and create a synchronization packet from the mastering site using multitool syncreplica -export so the site where the element creation is going to happen receives the mkbranch operation.
You see that kind of operation in IBM technote swg21118471.
On Windows, this setting can also help preventing this situation:
cleardlg.exe/options/Operations tab/Advanced Options:
When creating an element in a replicated VOB,
make current replica the master of all newly created branches.
I also had this exact issue when trying to checkout a file to modify.
I was able to create a view, but when I tried to checkout a file it kept complaining about:
Error checking out '<file>'.
Unable to perform operation "make branch" in replica "<branch>" of VOB "<vob>".
Master replica of branch type "<type>" is "<X>"
Unable to check out "<file>"
This was fixed by changing the ClearCase Registry Server to the correct host, and then re-creating the View.
I currently have my Greenplum database installed and running on a server. I have attached a new hard disk and have simply copied the master directory and all of the segment directories over because I want to point my database to the data on the new hard disk.
I have changed the environment variable MASTER_DATA_DIRECTORY to point to the new master directory, however I cannot figure out how to point to the new segment data directories. How can I point to the new directories so that when I run gpstart, my database starts up pointing to the data on the new hardware?
Thanks
Out of the box Greenplum does not support moving its directories. But it can be done this way:
Move the directories and in their old locations create symbolic links to the new locations. For instance, if previously you used "/data/master" directory and switched to "/data2/master" you can easily remove "/data/master" directory and replace it with symbolic link "/data/master -> /data2/master"
More complicated and not recommended approach. Greenplum stores filespace locations in pg_filespace_entry table. You should start the Greenplum in restricted mode, edit this table ("set allow_system_table_mods=DML; update pg_filespace_entry set ..."), stop the Greenplum (the stop might fail, you should manually stop each segment with "pg_ctl -D stop"), move the directories
Regardless of the approaches, you should backup the DB. If this is test environment, I'd recommend you to just remove the old system with "gpdeletesystem" and init it anew in new directories
I built an Analysis that displayed Results, error free. All is well.
Then, I added some filters to existing criteria sets. I also copied an existing criteria set, pasted it, and modified it's filters. When I try to display results, I see a View Display Error.
I’d like to revert back to that earlier functional version of the analyses, hopefully without manually undoing the all of filter & criteria changes I made since then.
If you’ve seen a feature like this, I’d like to hear about it!
Micah-
Great question. There are many times in the past when we wished we had some simple SCM on the Oracle BI Web Catalog. There is currently no "out of the box" source control for the web catalog, but some simple work-arounds do exist.
If you have access server side where the web catalog lives you can start with the following approach.
Oracle BI Web Catalog Version Control Using GIT Server Side with CRON Job:
Make a backup of your web catalog!
Create a GIT Repository in the web cat
base directory where the root dir and root.atr file exist.
Initial commit eveything. ( git add -A; git commit -a -m
"initial commit"; git push )
Setup a CRON job to run a script Hourly,
Minutely, etc that will tell GIT to auto commit any
adds/deletes/modifications to your GIT repository. ( git add -A; git
commit -a -m "auto_commit_$(date +"%Y-%m-%d_%T")"; git push )
Here are the issues with this approach:
If the CRON runs hourly, and an Analysis changes 3 times in the hour
you'll be missing some versions in there.
No actual user submitted commit messages.
Object details such as the Objects pretty "Name" (caption), Description (user populated on Save Dialog), ACLs, and object custom properties are stored in a binary file format. These files have the .atr extension. The good news though is that the actual object definition is stored in a plain text file in XML (Without the .atr).
Take this as a baseline, and build upon it. Here is how you could step it up!
Use incron or other inotify based file monitoring such as ruby
based guard. Using this approach you could commit nearly
instantly anytime a user saves an object and the BI server updates
the file system.
Along with inotify, you could leverage the BI Soap API to retrieve the actual object details such as Description. This would allow you to create meaningfull commit messages. Or, parse the binary .atr file and pull the info out. Here are some good links to learn more about Web Cat ATR files: Link (Keep in mind this links are discussing OBI 10g. The binary format for 11G has changed slightly.)