I have access to a repository where a team writes parquet file (without partitioning them), using delta (i.e there is a delta log in this repo). I have no access to the table itself though. To create a dataframe from those parquet, I am using the below code:
spark.read.format('delta').load(repo)
Executing this loads the entire dataframe, regardless of the delta log. How should I proceed to load the latest version of my data?
Related
I create table on Hadoop cluster using PySpark SQL:spark.sql("CREATE TABLE my_table (...) PARTITIONED BY (...) STORED AS Parquet") and load some data with: spark.sql("INSERT INTO my_table SELECT * FROM my_other_table"), however the resulting files do not seem to be Parquet files, they're missing ".snappy.parquet" extension.
The same problem occurs when repeating those steps in Hive.
But surprisingly when I create table using PySpark DataFrame: df.write.partitionBy("my_column").saveAsTable(name="my_table", format="Parquet")
everything works just fine.
So, my question is: what's wrong with the SQL way of creating and populating Parquet table?
Spark version 2.4.5, Hive version 3.1.2.
Update (27 Dec 2022 after #mazaneicha answer)
Unfortunately, there is no parquet-tools on the cluster I'm working with, so the best I could do is to check the content of the files with hdfs dfs -tail (and -head). And in all cases there is "PAR1" both at the beginning and at the end of the file. And even more - the meta-data of parquet version (implementation):
Method # of files Total size Parquet version File name
Hive Insert 8 34.7 G Jparquet-mr version 1.10.0 xxxxxx_x
PySpark SQL Insert 8 10.4 G Iparquet-mr version 1.6.0 part-xxxxx-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx.c000
PySpark DF insertInto 8 10.9 G Iparquet-mr version 1.6.0 part-xxxxx-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx.c000
PySpark DF saveAsTable 8 11.5 G Jparquet-mr version 1.10.1 part-xxxxx-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx-c000.snappy.parquet
(To create the same number of files I used "repartition" with df, and "distribute by" with SQL).
So, considering the above mentioned, it's still not clear:
Why there is no file extension in 3 out of 4 cases?
Why files created with Hive are so big? (no compression, I suppose).
Why PySpark SQL and PySpark Dataframe versions/implementations of parquet differ and how set them explicitly?
File format is not defined by the extension, but rather by the contents. You can quickly check if format is parquet by looking for magic bytes PAR1 at the very beginning and the very end of a file.
For in-depth format, metadata and consistency checking, try opening a file with parquet-tools.
Update:
As mentioned in online docs, parquet is supported by Spark as one of the many data sources via its common DataSource framework, so that it doesn't have to rely on Hive:
"When reading from Hive metastore Parquet tables and writing to non-partitioned Hive metastore Parquet tables, Spark SQL will try to use its own Parquet support instead of Hive SerDe for better performance..."
You can find and review this implementation in Spark git repo (its open-source! :))
I have data written in Delta on HDFS. From what I understand, Delta is storing the data as parquet, just has an additional layer over it with advanced features.
But when reading data with Pyspark, I get a different result if dataframe is read with spark.read.parquet() or spark.read.format('delta').load()
df = spark.read.format('delta').load("my_data")
df.count()
> 184511389
df = spark.read.parquet("my_data")
df.count()
> 369022778
As you can see the difference is quite big.
Is there something I misunderstood about delta vs parquet?
Pyspark version is 2.4.
The most probable explanation is that you wrote into the Delta two times using the overwrite option. But Delta is versioned data format - when you use overwrite, it doesn't delete previous data, it just writes new files, and don't delete files immediately - they are just marked as deleted in the manifest file that Delta uses. And when you read from Delta, it knows which files are deleted, or not, and read only actual data. Actual deletion of the data files happens when you're performing VACUUM on Delta lake.
But when you read with Parquet, it doesn't have information about deleted files, so it reads everything that you have in directory, so you get twice as many rows.
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 am trying to save DataFrame in Amazon S3 parquet folder using date as partition key. I am loading data day by day.
The first time I save it I see partition folder (i.e. "txDate=20160714").
When I am processing next files, they all go to "txDate=__HIVE_DEFAULT_PARTITION__": see parquet Hive partitions
txDate is int
I am using Databricks platform, Apache Spark 1.6.2 and Hadoop 2.
My code is in Python (Pyspark)
# initial save
df_newTx.write.partitionBy(['txDate']).format('parquet').mode('append').save("/mnt/dm.Inv/f_Tx.parquet")
# incremental save
df_tx_all.write.partitionBy(['txDate']).format('parquet').mode('append').save("/mnt/dm.Inv/f_Tx.parquet")
I am using spark streaming to write the aggregated output as parquet files to the hdfs using SaveMode.Append. I have an external table created like :
CREATE TABLE if not exists rolluptable
USING org.apache.spark.sql.parquet
OPTIONS (
path "hdfs:////"
);
I had an impression that in case of external table the queries should fetch the data from newly parquet added files also. But, seems like the newly written files are not being picked up.
Dropping and recreating the table every time works fine but not a solution.
Please suggest how can my table have the data from newer files also.
Are you reading those tables with spark?
if so, spark caches parquet tables metadata (since schema discovery can be expensive)
To overcome this, you have 2 options:
Set the config spark.sql.parquet.cacheMetadata to false
refresh the table before the query: sqlContext.refreshTable("my_table")
See here for more details: http://spark.apache.org/docs/latest/sql-programming-guide.html#hive-metastore-parquet-table-conversion