abf | Abstract Binary Format Manipulation - ELF , PE and Mach-O | Parser library

The dataset writer was changed significantly in 7.0.0. Previously, it would always create 1 file per partition. Now there are a couple of settings that could cause it to write multiple files. Furthermore, it looks like you are ending up with a lot of small row groups which is not ideal and probably the reason the one-step process is both slower and larger.

The first significant setting is max_open_files. Some systems limit how many file descriptors can be open at one time. Linux defaults to 1024 and so pyarrow attempts defaults to ~900 (with the assumption that some file descriptors will be open for scanning, etc.) When this limit is exceeded pyarrow will close the least recently used file. For some datasets this works well. However, if each batch has data for each file this doesn't work well at all. In that case you probably want to increase max_open_files to be greater than your number of partitions (with some wiggle room because you will have some files open for reading too). You may need to adjust OS-specific settings to allow this (generally, these OS limits are pretty conservative and raising this limit is fairly harmless).

I'll still wonder why the row_groups is so different if they're so different when setting those parameters but I'll defer that question.

In addition, the 7.0.0 release adds min_rows_per_group, max_rows_per_group and max_rows_per_file parameters to the write_dataset call. Setting min_rows_per_group to something like 1 million will cause the writer to buffer rows in memory until it has enough to write. This will allow you to create files with 1 row group instead of 188 row groups. This should bring down your file size and fix your performance issues.

However, there is a memory cost associated with this which is going to be min_rows_per_group * num_files * size_of_row_in_bytes.

The remaining question is, how to set version="2.6" and data_page_version="2.0" in write_dataset?

The write_dataset call works on several different formats (e.g. csv, ipc, orc) and so the call only has generic options that apply regardless of format.

Format-specific settings can instead be set using the file_options parameter:

In the following I assume that the dependencies given are a cover of the dependencies of R.

(a) Do the original functional dependencies apply in R1, R2, and R3?

When one decomposes a relation, not necessarily the dependencies of a cover applies to the decomposed relations. This happens when a decomposed relation does non contains all the attributes of a dependency. For instance, in your example, DF -> BC does not hold in any of R1, R2, R3, since the attributes DFBC are not all present in a single relation (we know that functional dependencies are meaningful only inside a relation).

This not necessarily means that the decomposition suffers from a “loss of dependency”, since the definition is more complex: A decomposition of a relation schema R with attributes A and cover of dependencies F preserves the dependencies if and only if the union of the projections of the dependencies of F over the decomposed relation is a cover of F.

In Ullman, Principles of Database Systems, Computer Science Press, 1983, an algorithm is shown to compute the closure of the union of the projection of a set of dependency over a decomposition. In your particular case, by applying that algorithm we can find that the dependency DF -> BC is actually lost.

(b) Is this decomposition in 3NF?

Here you answer is correct, since the third decomposed relation is not in 3NF. As you have correctly pointed out, the candidate keys for this relation are {AEF, AFH}, while in the relation the dependency AF -> B hold, and this is a dependency that violates the 3NF since AF is not a superkey and B is not a prime attribute.

In the Object Explorer (left) right click "Databases" and select "Restore Database". In the dialog that pops up, be sure on the left "General" is selected and you should see the "Device" option then.

You are receiving this due to lack of permissions. You can observe while creating the synapse workspace it do says that we need additional user access roles that needed to be done. One must be assigned to Storage Blob Data Contributor role on the storage account in order to access the adls workspace.

Here are the steps to Grant permissions to managed identity in Synapse workspace

REFERENCES:

• 403 StatusDescription
• Not able to create mount on Storage Gen2 from Azure DataBricks [wasbs vs abfss]

Solution 1

You can use a single pattern for this, but you will need to check which group matched in the code to determine where the necessary details are captured.

The regex will look like

Have a look at itertools.combinations. It returns all possible combinations of a given length for a given iterable. You'll have to loop over all possible lengths.

This is possible by renaming the single parquet file in the dataset so that it always has the same file name, that way the export task will overwrite the previous file in the external system.

This can be done using raw file system access. The write_single_named_parquet_file function below validates its inputs, creates a file with a given name in the output dataset, then copies the file in the input dataset to it. The result is a schemaless output dataset that contains a single named parquet file.

Notes

• The build will fail if the input contains more than one parquet file, as pointed out in the question, calling .coalesce(1) (or .repartition(1)) is necessary in the upstream transform
• If you require transaction history in your external store, or your dataset is much larger than 512 MB this method is not appropriate, as only the latest version is kept, and you likely want multiple parquet files for use in your downstream system. The createTransactionFolders (put each new export in a different folder) and flagFile (create a flag file once all files have been written) options can be useful in this case.
• The transform does not require any spark executors, so it is possible to use @configure() to give it a driver only profile. Giving the driver additional memory should fix out of memory errors when working with larger datasets.
• shutil.copyfileobj is used because the 'files' that are opened are actually just file objects.

Full code snippet

example_transform.py

The F# Language specification provides a formal description of language elements. For the compare function see p. 173 "8.15.6 Behavior of Hash, =, and Compare", where the behavior is described in pseudocode to achieve the following objectives:

• Ordinal comparison for strings
• Structural comparison for arrays
• Natural ordering for native integers (which do not support System.IComparable)

Structural comparison, an important concept in functional programming, does apply to tuples, lists, options, arrays, and user-defined record, union, and struct types whose constituent field types permit structural equality, hashing, and comparison.

For strings, the comparison relies on System.String.CompareOrdinal, whose return values are described under the System.String.Compare method:

Less than zero: strA precedes strB in the sort order.

Zero: strA occurs in the same position as strB in the sort order.

Greater than zero: strA follows strB in the sort order.

ABFS is a "real" file system, so the S3A zero rename committers are not needed. Indeed, they won't work. And the client is entirely open source - look into the hadoop-azure module.

the ADLS gen2 store does have scale problems, but unless you are trying to commit 10,000 files, or clean up massively deep directory trees -you won't hit these. If you do get error messages about Elliott to rename individual files and you are doing Jobs of that scale (a) talk to Microsoft about increasing your allocated capacity and (b) pick this up https://github.com/apache/hadoop/pull/2971

This isn't it. I would guess that actually you have multiple jobs writing to the same output path, and one is cleaning up while the other is setting up. In particular -they both seem to have a job ID of "0". Because of the same job ID is being used, what only as task set up and task cleanup getting mixed up, it is possible that when an job one commits it includes the output from job 2 from all task attempts which have successfully been committed.

I believe that this has been a known problem with spark standalone deployments, though I can't find a relevant JIRA. SPARK-24552 is close, but should have been fixed in your version. SPARK-33402 Jobs launched in same second have duplicate MapReduce JobIDs. That is about job IDs just coming from the system current time, not 0. But: you can try upgrading your spark version to see if it goes away.

My suggestions

1. make sure your jobs are not writing to the same table simultaneously. Things will get in a mess.
2. grab the most recent version spark you are happy with