Based on the FAQ at Parse.com:
What is the difference between database storage and file storage?
Database storage refers to data stored as Parse Objects, which are
limited to 128 KB in size. File storage refers to static assets that
are stored using the Parse File APIs, typically images, documents, and
other types of binary data.
Just want some clarification here:
So the Strings, Arrays etc created are considered as Parse Objects and would fall under the database storage, also the URL of the file will be considered under the database storage since it is a Parse Object. But the actual files itself are considered under File Storage?
Thanks.
Yes. Any file that you upload to Parse goes to the File storage, the rest is stored in the database including the URL of such files.
Related
I have a large line delimited (not comma) CSV file (1.2 million lines - 140mb) that contains both data and metadata from a test. The first 50 or so lines are metadata which I need to extract to populate an SQL table.
I have built a Logic App which uses the Azure Blob Storage connector as a trigger. This CSV file is copied into the blob and it triggers the app to do it's stuff. For small files under 50mb this works fine however I get this error for larger files.
InvalidTemplate. Unable to process template language expressions in action 'GetMetaArray' inputs at line '0' and column '0': 'The template language function 'body' cannot be used when the referenced action outputs body has large aggregated partial content. Actions with large aggregated partial content can only be referenced by actions that support chunked transfer mode.'.
The output query is take(split(body('GetBlobContent'), decodeUriComponent('%0D%0A')),100)
The query allows me to put the line delimited meta data into an array so I can perform some queries against it to extract data I convert into variables and use them to check the file for consistency (e.g meta data must meet certain criteria)
I understand that the "Get Blob Content V2" supports chunking natively however from the error it seems like I cannot use the body function to return my array. Can anyone offer any suggestions how I get around this issue? I only need to use a tiny proportion of this file
Thanks Jonny
One question about the read efficiency when using Azure Blob Storage. Whehter it's faster to read from multiple small size file (e.g. 5MB) or it's faster to read from a large file (e.g. > 200MB)?
In my current project, I need to persist the stream data in Azure Blob Storage in Avro format and read it afterwards. For example, I can either persist the data every 15min into a single Avro file and that will generate 4*24 = 96 files for one day. Or I can use AppendBlob to append all data to one file, which will generate a single Avro file for one day. When reading the data of past few days from the Blob Stroage, which case could be more efficient?
I need to archive obsolete data from NoSql storage into Azure Blob storage and cannot find the way to do it: whether save it as json-files in Blolb storage or save it in Tables.
Key points: documents won't be modified, but they may be requested by name (ex. name is identifier).
UPDATE1:
All documents has unique names (GUID with salt).
Estimated documents number is about one million.
If you want that: documents won't be modified, but they may be requested by name. Then you should store them in blob store.
In blob storage, there is a feature named immutable storage, you can set it and the blob will be read-only.
I have been manually partitioning files with pandas (creating an index or multi-index and then writing a separate parquet file for each index in a loop) to Azure Blob.
However, when reading the docs for pyarrow, I see that it is possible to create a 'dataset' which includes a folder structure for partitioned data. https://arrow.apache.org/docs/python/parquet.html
The example for the Monthly / daily folder is exactly what I am trying to achieve.
dataset_name/
year=2007/
month=01/
0.parq
1.parq
...
month=02/
0.parq
1.parq
...
month=03/
...
year=2008/
month=01/
...
fs = pa.hdfs.connect(host, port, user=user, kerb_ticket=ticket_cache_path)
pq.write_to_dataset(table, root_path='dataset_name',
partition_cols=['one', 'two'], filesystem=fs)
Can I do this with Azure Blob (or Minio which uses S3 and wraps over my Azure Blob storage)? My ultimate goal is to only read files which make sense for my 'query'.
Just per my experience and based on your current environment Linux on Azure VM, I think there are two solutions can read partition parquet files from Azure Storage.
Follow the section Reading a Parquet File from Azure Blob storage of the document Reading and Writing the Apache Parquet Format of pyarrow, manually to list the blob names with the prefix like dataset_name using the API list_blob_names(container_name, prefix=None, num_results=None, include=None, delimiter=None, marker=None, timeout=None) of Azure Storgae SDK for Python as the figure below, then to read these blobs one by one like the sample code to dataframes, finally to concat these dataframes to a single one.
Try to use Azure/azure-storage-fuse to mount a container of Azure Blob Storage to your Linux filesystem, then you just need to follow the document section Reading from Partitioned Datasets to read the Partitioned Dataset locally from Azure Blob Storage.
This is not a question of a code, I need to extract some BLOB data from an Oracle database using python script. My question is what are the steps in dealing with BLOB data and how to read as images, videos and text? Since I have no access to the database itself, is it possible to know the type of BLOBs stored if it is pictures, videos or texts? Do I need encoding or decoding in order to tranfer these BLOBs into .jpg, .avi or .txt files ? These are very basic questions but I am new to programming so need some help to find a starting point :)
If you have a pure BLOB in the database, as opposed to, say, an ORDImage that happens to be stored in a BLOB under the covers, the BLOB itself has no idea what sort of binary data it contains. Normally, when the table was designed, a column would be added that would store the data type and/or the file name.