may I know is it correct that the delta lake file extension is *.snappy.parquet ??
I use the code
df.write.format('delta').save(blobpath)
any one has the idea ???
Yes, that's correct. Delta is built on top of the Parquet files as storage format, and adds additional capabilities such as transactions, ...
P.S. this is answered in the Delta's FAQ
Related
I have a table on dbfs I can read with pyspark, but I only need to know the length of it (nrows). I know I could just read the file and do a table.count() to get it, but that would take some time.
Is there a better way to solve this?
I am afraid not.
Since you are using dbfs, I suppose you are using Delta format with Databricks. So, theoretically, you could check the metastore, but:
The metastore is not the source of truth about the latest information
of a Delta table
https://docs.delta.io/latest/delta-batch.html#control-data-location
I am importing fact and dimension tables from SQL Server to Azure Data Lake Gen 2.
Should I save the data as "Parquet" or "Delta" if I am going to wrangle the tables to create a dataset useful for running ML models on Azure Databricks ?
What is the difference between storing as parquet and delta ?
Delta is storing the data as parquet, just has an additional layer over it with advanced features, providing history of events, (transaction log) and more flexibility on changing the content like, update, delete and merge capabilities. This link delta explains quite good how the files organized.
One drawback that it can get very fragmented on lots of updates, which could be harmful for performance. AS the AZ Data Lake Store Gen2 is anyway not optimized for large IO this is not really a big problem. Some optimization on the parquet format though will not be very effective this way.
I would use delta, just for the advanced features. It is very handy if there is a scenario where the data is updating over time, not just appending. Specially nice feature that you can read the delta tables as of a given point in time they existed.
SQL as of syntax
This is useful for having consistent training sets (to always have the same training dataset without separating to individual parquet files). In case for the ML models handling delta format as input may could be problematic, as likely only few frameworks will be able to read it in directly, so you will need to convert it during some pre-processing step.
Delta Lake uses versioned Parquet files to store your data in your cloud storage. Apart from the versions, Delta Lake also stores a transaction log to keep track of all the commits made to the table or blob store directory to provide ACID transactions.
Reference : https://learn.microsoft.com/en-us/azure/databricks/delta/delta-faq
As per the other answers Delta Lake is a feature layer over Parquet.
Consider - do you need Delta features? if you are just reading the data & wrangling elsewhere Delta is just extra complexity for little additional benefit.
Also Parquet is compatible with almost every data system out there, Delta is widely adopted but not everything can work with Delta.
Consider using parquet if you don't need a transaction log.
We extract data daily and replace it with the Delta file. However, it re-creates the same number of parquet files every time though there is a minor change to data.
I have saved one dataframe in my delta lake, below is the command:
df2.write.format("delta").mode("overwrite").partitionBy("updated_date").save("/delta/userdata/")
Also I can load and see the delta lake /userdata:
dfres=spark.read.format("delta").load("/delta/userdata")
but here , I have one doubt like when I am moving several parquet files from blob to delta lake creating dataframe, then how some one else would know which file I have moved and how he can work on those delta, is there any command to list all the dataframes in delta lake in databricks?
Break down the problem into:
Find the paths of all tables you want to check. Managed tables in the default location are stored at spark.conf.get("spark.sql.warehouse.dir") + s"/$tableName". If you have external tables, it is better to use catalog.listTables() followed by catalog.getTableMetadata(ident).location.getPath. Any other paths can be used directly.
Determine which paths belong to Delta tables using DeltaTable.isDeltaTable(path).
Hope this helps.
I started going through DELTA LAKE file format, is hive capable of reading data from this newly introduced delta file format? If so could you please let me know the serde you were using.
Hive support is available with Delta Lake file format. First, step is to add the jars from https://github.com/delta-io/connectors, in our hive path. And then create a table using following format.
CREATE EXTERNAL TABLE test.dl_attempts_stream
(
...
)
STORED BY 'io.delta.hive.DeltaStorageHandler'
LOCATION
Delta Format picks up partition by default, so no need to mention partition while creating a table.
NOTE: If data is being inserted via a Spark job, please provide hive-site.xml, and enableHiveSupport in Spark Job, to create Delta Lake table in Hive.
I want to load data from On Premise SQL SERVER to blob storage with copy activity in ADF, the target file is parquet, the size of this one is 5 Gb.
The pipeline work well and he wrote one parquet file, now i need to split this file in multiple parquet file to optimise loading data with Poly base and for another uses.
With Spark we can partition file in multiple file by this syntaxe :
df.repartition(5).write.parquet("path")
Short question, short answer.
Partitioned data: https://learn.microsoft.com/en-us/azure/data-factory/how-to-read-write-partitioned-data
Parquet format: https://learn.microsoft.com/en-us/azure/data-factory/format-parquet
Blob storage connector: https://learn.microsoft.com/en-us/azure/data-factory/connector-azure-blob-storage
Hope this helped!