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.
Related
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 found a ton of examples showing how to Merge data using Databricks Delta Table Merge to load data to SQL DB. However, I'm trying to find examples whereby trying to load data to SQL DB without Databricks Delta Merge fails.
This is because I'm having trouble getting my head around knowing a situation where I should be using Databricks Delta Merge.
Therefore, can someone point me to a link showing where loading data to SQL DB from Databricks would fail withou Databricks Delta Merge, alternatively steps I would have to take to merge without Databricks Delta Lake Merge?
I was going through delta lake and came across a configuration spark.databricks.delta.snapshotPartitions however not quite sure what this is used for? Can't find this in delta lake documentation as well.
In delta lake github found below code, but not sure how this property works
val DELTA_SNAPSHOT_PARTITIONS =
buildConf("snapshotPartitions")
.internal()
.doc("Number of partitions to use when building a Delta Lake snapshot.")
.intConf
.checkValue(n => n > 0, "Delta snapshot partition number must be positive.")
.createOptional
Delta Lake uses Spark to process the transaction logs in the _delta_log directory. When Delta Lake loads the transaction logs, it will replay logs to generate the current state of the table which is called Snapshot. There is a repartition operation in this step. You can use spark.databricks.delta.snapshotPartitions to config how many partitions to use in the repartition operation. When your table metadata grows, you may need to increase this config so that each partition of the table metadata can be fit into the executor memory.
I use ADF to ingest the data from SQL server to ADLS GEN2 in a Parquet Snappy format, But the size of the file in sink goes upto 120 GB, The size causes me a lot of problem when I read this file in Spark and join the data from this file with many other Parquet files.
I am thinking to use Delta lake's unmanage table with the location pointing to the ADLS location, I am able to create an UnManaged table if I don't specify any partition using this
" CONVERT TO DELTA parquet.PATH TO FOLDER CONTAINING A PARQUET FILE(S)"
But if I would want to partition this file for query optimization
" CONVERT TO DELTA parquet.PATH TO FOLDER CONTAINING A PARQUET FILE(S), PARTITIONED_COLUMN DATATYPE"
It gives me error like the one mentioned in the screenshot (find the attachment).
Error in Text :-
org.apache.spark.sql.AnalysisException: Expecting 1 partition column(s): [<PARTITIONED_COLUMN>], but found 0 partition column(s): [] from parsing the file name: abfss://mydirectory#myADLS.dfs.core.windows.net/level1/Level2/Table1.parquet.snappy;
There is no way that I can create this Parquet file using ADF with partition details (Am open for suggestions)
Am I giving a wrong Syntax or this can be even done?
Ok, I found the answer to this. While you convert parquet files to delta using the above approach, Delta would look for the correct directory structure with partition information along with the name of the column mentioned in "Partitioned By" clause.
For E.g, I have a folder called /Parent, inside this I have a directory structure with partition information, the partitioned parquet files are kept one level further inside the partitioned folders, the folder names are like this
/Parent/Subfolder=0/part-00000-62ef2efd-b88b-4dd1-ba1e-3a146e986212.c000.snappy.parquet
/Parent/Subfolder=1/part-00000-fsgvfabv-b88b-4dd1-ba1e-3a146e986212.c000.snappy.parquet
/Parent/Subfolder=2/part-00000-fbfdfbfe-b88b-4dd1-ba1e-3a146e986212.c000.snappy.parquet
/Parent/Subfolder=3/part-00000-gbgdbdtb-b88b-4dd1-ba1e-3a146e986212.c000.snappy.parquet
in this case, subfolder is the partitions created inside parent.
CONVERT TO DELTA parquet./Parent/ partitioned by (Subfolder INT)
will just take this directory structure and convert the whole partitioned data to delta and will store the partitioned information in metastore.
Summary:- This command is only to utilize already created partitioned Parquet files. To create partition on single Parquet file you would have to take different route, Which I can explain you later if you are interested ;)
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.