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ALTER LARGE OBJECT changes the definition of a large object.

You must own the large object to use ALTER LARGE OBJECT. To alter the owner, you must also be a direct or indirect member of the new owning role. (However, a superuser can alter any large object anyway.) Currently, the only functionality is to assign a new owner, so both restrictions always apply.

OID of the large object to be altered

This section introduces different types of data that you encounter when developing applications and discusses which kinds of data are suitable for large objects.

This kind of data is complex in nature and is suited for the object?relational features of the Oracle database such as collections, references, and user-defined types.

Large objects are suitable for these last two kinds of data: semi-structured data and unstructured data. Large objects features allow you to store these kinds of data in the database as well as in operating system files that are accessed from the database.

Binary File objects (BFILE datatypes) can also store character data. You can use BFILEs to load read-only data from operating system files into CLOB or NCLOB instances that you then manipulate in your application.

Persistent LOBs use copy semantics and participate in database transactions. You can recover persistent LOBs in the event of transaction or media failure, and any changes to a persistent LOB value can be committed or rolled back. In other words, all the Atomicity Consistency Isolation Durability (ACID) properties that pertain to using database objects pertain to using persistent LOBs.

External LOBs are data objects stored in operating system files, outside the database tablespaces. The database accesses external LOBs using the SQL datatype BFILE. The BFILE datatype is the only external LOB datatype.

Data that is loaded into other large object types, such as a BLOB or CLOB where the data can then be manipulated.

An important feature of Blob, Clob, and NClob Java objects is that you can manipulate them without having to bring all of their data from the database server to your client computer. Some implementations represent an instance of these types with a locator (logical pointer) to the object in the database that the instance represents. Because a BLOB, CLOB, or NCLOB SQL object may be very large, the use of locators can make performance significantly faster. However, other implementations fully materialize large objects on the client computer.

If you want to bring the data of a BLOB, CLOB, or NCLOB SQL value to the client computer, use methods in the Blob, Clob, and NClob Java interfaces that are provided for this purpose. These large object type objects materialize the data of the objects they represent as a stream.

The following excerpt from ClobSample.addRowToCoffeeDescriptions adds a CLOB SQL value to the table COFFEE_DESCRIPTIONS. The Clob Java object myClob contains the contents of the file specified by fileName.

The following line retrieves a stream (in this case a Writer object named clobWriter) that is used to write a stream of characters to the Clob Java object myClob. The method ClobSample.readFile writes this stream of characters; the stream is from the file specified by the String fileName. The method argument 1 indicates that the Writer object will start writing the stream of characters at the beginning of the Clob value:

The ClobSample.readFile method reads the file line-by-line specified by the file fileName and writes it to the Writer object specified by writerArg:

This is thread-safe of course and also has a side-benefit for caching: since Pile makes a new instance on every Get(), you can safely give-out a copy of object for requesting thread, even if they compete for the same object (same PilePointer). This is like an in functional languages when a worker takes the order, mutates it into something new then passes along the chain - no need to lock().

An important note to keep in mind: When you compare the test results from below against, say Redis or Memcached, ask yourself a question: am I comparing apples to apples? Does your API give you back a .NET object or a string you need to parse somehow to access ? If it gives you a string or some other form of data, please do not forget to account for serialization time to convert the raw data into a usable .NET object. In other words, a string nameFrank Drebinsalary does not allow you to access without parsing. Pile returns you a real CLR object - nothing to parse, so the ser/deser time is already accounted for in the numbers below. When Pile stores byte[] or strings, its performance needs to be multiplied at least five, if not ten, times.

Stable operation with 10 million objects in RAM before slowdowns start

Speed deteriorates (SpeeDet) after: 25 million objects added

Writes: average 0.8 million objects / sec (starts at millions+/sec then slows down significantly after SpeeDet), interrupted by GC pauses

Reads while writing: 1 million objects / sec interrupted by GC pauses

Stable operation with 1,000 million serialized objects in RAM (yes, ONE BILLION objects)

Slow down after: 600 million objects (as they stop fitting in 64 GB physical RAM)

Writes: 0.5 million objects / sec without interruptions

Reads while writing: 0.7 million objects / sec without interruptions

Garbage Collector stop-all pauses at 600M objects: none

But when you think about it, it really isnt handle tens of millions of objects if they stick around.

A Big Memory Pile Cache is abstracted into the interface that supports priority, maximum age, absolute expiration timestamps and memory limits. When you approach a limit, the object starts to get overwritten if their priorities allow.

The cache evicts old data (auto-delete) and expires objects at a certain timestamp if it was set. It also clones detailed table settings from a cache-wide setup where everything is configurable (index size, LWM, HWM, grow/shrink percentages etc.).

Here you have it. 1+ billion objects are allocated on a Pile. The write throughput is now around a paltry 300,000 inserts a second from 10 threads. This is because we have also allocated 80+GB on a 64 GB machine. Actually, I was surprised swapping did not kill it altogether, it still works, AND full-scan GC is 11 ms! This machine is 3 Ghz 6 core i7 with 64GB physical running Windows 7 64bit.

Documentation for GitLab instance administrators is under LFS administration doc. Requirements Git LFS is supported in GitLab starting with version 8.2 Git LFS must be enabled under project settings Git LFS client version 1.0.1 and up Known limitations Git LFS v1 original API is not supported since it was deprecated early in LFS development When SSH is set as a remote, Git LFS objects still go through HTTPS Any Git LFS request will ask for HTTPS credentials to be provided so a good Git credentials store is recommended Git LFS always assumes HTTPS so if you have GitLab server on HTTP you will have to add the URL to Git configuration manually (see troubleshooting) Note: With 8.12 GitLab added LFS support to SSH. The Git LFS communication still goes over HTTP, but now the SSH client passes the correct credentials to the Git LFS client, so no action is required by the user. Using Git LFS

Once a certain file extension is marked for tracking as a LFS object you can use Git as usual without having to redo the command to track a file with the same extension: cp ~/tmp/debian.iso ./ copy a large file into the current directory git add . add the large file to the project git commit -am commit the file meta data git push origin master sync the git repo and large file to the GitLab server

If you already cloned the repository and you want to get the latest LFS object that are on the remote repository, such as for a branch from origin: git lfs fetch origin master

Read the documentation on how to migrate an existing Git repository with Git LFS. Removing objects from LFS

To remove objects from LFS: Use git filter-repo to remove the objects from the repository. Delete the relevant LFS lines for the objects you have removed from your .gitattributes file and commit those changes. File Locking

See the documentation on File Locking. LFS objects in project archives Version history Support for including Git LFS blobs inside project source downloads was introduced in GitLab 13.5. It`s deployed behind a feature flag, disabled by default. To use it in GitLab self-managed instances, ask a GitLab administrator to enable it. Warning: This feature might not be available to you. Check the version history note above for details.

Prior to GitLab 13.5, project source downloads would include Git LFS pointers instead of the actual objects.

Starting with GitLab 13.5, these pointers are converted to the uploaded LFS object if the include_lfs_blobs_in_archive feature flag is enabled.

Technical details about how this works can be found in the development documentation for LFS. Enable or disable LFS objects in project archives

LFS objects in project archives is under development and not ready for production use. It is deployed behind a feature flag that is disabled by default. GitLab administrators with access to the GitLab Rails console can enable it.

To disable it: Feature.disable(:include_lfs_blobs_in_archive) Troubleshooting error: Repository or object not found

There are a couple of reasons why this error can occur: You don’t have permissions to access certain LFS object

Check if you have permissions to push to the project or fetch from the project. Project is not allowed to access the LFS object

LFS object you are trying to push to the project or fetch from the project is not available to the project anymore. Probably the object was removed from the server. Local Git repository is using deprecated LFS API Invalid status for url : 501

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