I'm currently using jdbc to write data into an existing table.
jdbcDF.write
.mode(Savemode.append)
.jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
I'm currently using code similar to above. When given a wrong table name, it will create that table and input that table there as opposed to throwing an error. Is there anyway to stop that feature.
This behaviour is not supported by Spark. Your would need to write your own logic around it.
According to the ScalaDocs on Enumeration SaveMode you have the following options when writing Data to a sink:
Append: Append mode means that when saving a DataFrame to a data source, if data/table already exists, contents of the DataFrame are expected to be appended to existing data.
ErrorIfExists: ErrorIfExists mode means that when saving a DataFrame to a data source, if data already exists, an exception is expected to be thrown.
Ignore: Ignore mode means that when saving a DataFrame to a data source, if data already exists, the save operation is expected to not save the contents of the DataFrame and to not change the existing data.
Overwrite: Overwrite mode means that when saving a DataFrame to a data source, if data/table already exists, existing data is expected to be overwritten by the contents of the DataFrame.
Related
I am loading data from SQL Server to Delta lake tables. Recently i had to repoint the source to another table(same columns), but the data type is different in new table. This is causing error while loading data to delta table. Getting following error:
Failed to merge fields 'COLUMN1' and 'COLUMN1'. Failed to merge incompatible data types LongType and DecimalType(32,0)
Command i use to write data to delta table:
DF.write.mode("overwrite").format("delta").option("mergeSchema", "true").save("s3 path)
The only option i can think of right now is to enable OverWriteSchema to True.
But this will rewrite my target schema completely. I am just concerned about any sudden change in source schema that will replace existing target schema without any notification or alert.
Also i can't explicitly convert these columns because the databricks notebook i am using is a parametrized one used to to load data from source to Target(We are reading data from a CSV file that contain all the details about Target table, Source table, partition key etc)
Is there any better way to tackle this issue?
Any help is much appreciated!
I have always been under the impression that the following code create a Delta table,
data.write.format("delta").save("/path/to/delta-table")
This creates the files, sure, however, I noticed today that when I look at the Data section of Databricks, under the hive_metastore, this table does not show up.
In order for this table to show up there, I have to do something like,
CREATE TABLE some_table USING DELTA LOCATION "/path/to/delta-table"
What exactly is going on here? Was I wrong in my understanding that the .write operation creates a table? What is the difference between these commands?
DataFrameWriter has following methods:
def save(path: String): Unit
Saves the content of the DataFrame at the specified path.
def saveAsTable(tableName: String): Unit
Saves the content of the DataFrame as the specified table.
What you did by .save("/path/to/delta-table") was saving the data in delta format in the filesystem. In order for the table to be visible in data catalog (aka. metastore) you need to run CREATE TABLE providing the location.
You can write data using .saveAsTable("delta-table") - that would write the data under a path managed by the metastore and register the table in one step.
I am creating a process in spark scala within an ETL that checks for some events occurred during the ETL process. I start with an empty dataframe and if events occur this dataframe is filled with information ( a dataframe can't be filled it can only be joined with other dataframes with the same structure ). The thing is that at the end of the process, the dataframe that has been generated is loaded into a table but it can happen that the dataframe ends up being empty because no event has occured and I don't want to load a dataframe that is empty because it has no sense. So, I'm wondering if there is an elegant way to load the dataframe into the table only if it is not empty without using the if condition. Thanks!!
I recommend to create the dataframe anyway; If you don't create a table with the same schema, even if it's empty, your operations/transformations on DF could fail as it could refer to columns that may not be present.
To handle this, you should always create a DataFrame with the same schema, which means the same column names and datatypes regardless if the data exists or not. You might want to populate it with data later.
If you still want to do it your way, I can point a few ideas for Spark 2.1.0 and above:
df.head(1).isEmpty
df.take(1).isEmpty
df.limit(1).collect().isEmpty
These are equivalent.
I don't recommend using df.count > 0 because it is linear in time complexity and you would still have to do a check like df != null before.
A much better solution would be:
df.rdd.isEmpty
Or since Spark 2.4.0 there is also Dataset.isEmpty.
As you can see, whatever you decide to do, there is a check somewhere that you need to do, so you can't really get rid of the if condition - as the sentence implies: if you want to avoid creating an empty dataframe.
I wanted to change a column name of a Databricks Delta table.
So I did the following:
// Read old table data
val old_data_DF = spark.read.format("delta")
.load("dbfs:/mnt/main/sales")
// Created a new DF with a renamed column
val new_data_DF = old_data_DF
.withColumnRenamed("column_a", "metric1")
.select("*")
// Dropped and recereated the Delta files location
dbutils.fs.rm("dbfs:/mnt/main/sales", true)
dbutils.fs.mkdirs("dbfs:/mnt/main/sales")
// Trying to write the new DF to the location
new_data_DF.write
.format("delta")
.partitionBy("sale_date_partition")
.save("dbfs:/mnt/main/sales")
Here I'm getting an Error at the last step when writing to Delta:
java.io.FileNotFoundException: dbfs:/mnt/main/sales/sale_date_partition=2019-04-29/part-00000-769.c000.snappy.parquet
A file referenced in the transaction log cannot be found. This occurs when data has been manually deleted from the file system rather than using the table `DELETE` statement
Obviously the data was deleted and most likely I've missed something in the above logic. Now the only place that contains the data is the new_data_DF.
Writing to a location like dbfs:/mnt/main/sales_tmp also fails
What should I do to write data from new_data_DF to a Delta location?
In general, it is a good idea to avoid using rm on Delta tables. Delta's transaction log can prevent eventual consistency issues in most cases, however, when you delete and recreate a table in a very short time, different versions of the transaction log can flicker in and out of existence.
Instead, I'd recommend using the transactional primitives provided by Delta. For example, to overwrite the data in a table you can:
df.write.format("delta").mode("overwrite").save("/delta/events")
If you have a table that has already been corrupted, you can fix it using FSCK.
You could do that in the following way.
// Read old table data
val old_data_DF = spark.read.format("delta")
.load("dbfs:/mnt/main/sales")
// Created a new DF with a renamed column
val new_data_DF = old_data_DF
.withColumnRenamed("column_a", "metric1")
.select("*")
// Trying to write the new DF to the location
new_data_DF.write
.format("delta")
.mode("overwrite") // this would overwrite the whole data files
.option("overwriteSchema", "true") //this is the key line.
.partitionBy("sale_date_partition")
.save("dbfs:/mnt/main/sales")
OverWriteSchema option will create new physical files with latest schema that we have updated during transformation.
I need to run Spark SQL queries with my own custom correspondence from table names to Parquet data. Reading Parquet data to DataFrames with sqlContext.read.parquet and registering the DataFrames with df.registerTempTable isn't cutting it for my use case, because those calls have to be run before the SQL query, when I might not even know what tables are needed.
Rather than using registerTempTable, I'm trying to write an Analyzer that resolves table names using my own logic. However, I need to be able to resolve an UnresolvedRelation to a LogicalPlan representing Parquet data, but sqlContext.read.parquet gives a DataFrame, not a LogicalPlan.
A DataFrame seems to have a logicalPlan attribute, but that's marked protected[sql]. There's also a ParquetRelation class, but that's private[sql]. That's all I found for ways to get a LogicalPlan.
How can I resolve table names to Parquet with my own logic? Am I even on the right track with Analyzer?
You can actually retrieve the logicalPlan of your DataFrame with
val myLogicalPlan: LogicalPlan = myDF.queryExecution.logical