I'm having dataframe with 2 partitions, inserting into postgres table with overwrite method.
df.write \
.format("jdbc") \
.option("driver", POSTGRESQL_DRIVER) \
.option("url", url) \
.option("user", user) \
.option("password", password) \
.option("dbtable", "test_table") \
.mode("overwrite") \
.save()
Partitions Vector : (0, 1)
Partition 0 will insert first followed by partition 1, here partition 0 records are over writing in table. Only partition 1 records are available.
How can i insert or save two partitions without overwrite of previous partitions ?
I can see below two possible workarounds for this problem.
1) As part of the write provide one more option to truncate the table and then append so that old data will be truncated and new data frame will be appended. Every time you will have only new dataset this way.
df.write \
.format("jdbc") \
.option("driver", POSTGRESQL_DRIVER) \
.option("url", url) \
.option("user", user) \
.option("password", password) \
.option("dbtable", "test_table") \
.option("truncate", True) \
.mode("append") \
.save()
2) As part of the spark 2.3 we got new option where we can truncate only specific partition instead of all partitions. If you are using the latest version of spark, you can give try of this feature.
https://issues.apache.org/jira/browse/SPARK-20236
Hope this helps.
Related
I have been running spark-structured streaming with Kafka for the last 23 hours. And I could see Shuffle Read and Shuffle Write Increasing drastically and finally, the driver stopped due to"out of memory".
Data Pushing to Kafak is 3 json per second and Spark streaming processingTime='30 seconds'
spark = SparkSession \
.builder \
.master("spark://spark-master:7077") \
.appName("demo") \
.config("spark.executor.cores", 1) \
.config("spark.cores.max", "4") \
.config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \
.config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \
.config("spark.sql.warehouse.dir", "hdfs://172.30.7.36:9000/user/hive/warehouse") \
.config("spark.streaming.stopGracefullyOnShutdown", "true") \
.config("spark.executor.memory", '1g') \
.config("spark.scheduler.mode", "FAIR") \
.config("spark.driver.memory", '2g') \
.config("spark.sql.caseSensitive", "true") \
.config("spark.sql.shuffle.partitions", 8) \
.enableHiveSupport() \
.getOrCreate()
CustDf \
.writeStream \
.queryName("customerdatatest") \
.format("delta") \
.outputMode("append") \
.trigger(processingTime='30 seconds') \
.option("mergeSchema", "true") \
.option("checkpointLocation", "/checkpoint/bronze_customer/") \
.toTable("bronze.customer")
I am expecting this straming should be run alteast for 1 month continuously.
Spark is transforming json (Flattening the json) and insert into the delta table.
Please help me on this. weather i misssed any configuration ?
def QueryDB(sqlQuery):
jdbcUrl = mssparkutils.credentials.getSecret("param1","DBJDBCConntring","param3")
spark=SparkSession.builder.appName("show results").getOrCreate()
dbcdf = (spark.read.format("jdbc")
.option("url", jdbcUrl)
.option("query", sqlQuery)
.load()
)
return jdbcdf
df= QueryDB("INSERT INTO schema.table1 (column1, column2) output inserted.column1 values('one', 'two')")
df.show()
the notebook runs without any error but no rows are inserted. any suggestion or sample code to insert into table.
spark.read.format("jdbc") is to read JDBC. If you want to insert data to JDBC you'd want something like this
jdbcDF.write \
.format("jdbc") \
.option("url", "jdbc:postgresql:dbserver") \
.option("dbtable", "schema.tablename") \
.option("user", "username") \
.option("password", "password") \
.save()
I have a streaming dataframe from kafka and I need to pivot two columns. This is the code I'm currently using:
streaming_df = streaming_df.groupBy('Id','Date')\
.pivot('Var')\
.agg(first('Val'))
query = streaming_df.limit(5) \
.writeStream \
.outputMode("append") \
.format("memory") \
.queryName("stream") \
.start()
time.sleep(50)
spark.sql("select * from stream").show(20, False)
query.stop()
`
I recieve the following error:
pyspark.sql.utils.AnalysisException: Queries with streaming sources must be executed with writeStream.start()
pyspark version: 3.1.1
any ideas how to implement pivot with a streaming dataframe ?
The pivot transformation is not supported by Spark when applying to streaming data.
What you can do is to use the foreachBatch with a user defined function like this:
def apply_pivot(stream_df, batch_id):
# Here your pivot transformation
stream_df \
.groupBy('Id','Date') \
.pivot('Var') \
.agg(first('Val')) \
.write \
.format('memory') \
.outputMode('append') \
.queryName("stream")
query = streaming_df.limit(5) \
.writeStream \
.foreachBatch(apply_pivot) \
.start()
time.sleep(50)
spark.sql("select * from stream").show(20, False)
query.stop()
Let me know if it helped you!
I am receiving Kafka stream in pyspark. Currently I am grouping it by one set of fields and writing updates to database:
df = spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", config["kafka"]["bootstrap.servers"]) \
.option("subscribe", topic)
...
df = df \
.groupBy("myfield1") \
.agg(
expr("count(*) as cnt"),
min(struct(col("mycol.myfield").alias("mmm"), col("*"))).alias("minData")
) \
.select("cnt", "minData.*") \
.select(
col("...").alias("..."),
...
col("userId").alias("user_id")
query = df \
.writeStream \
.outputMode("update") \
.foreachBatch(lambda df, epoch: write_data_frame(table_name, df, epoch)) \
.start()
query.awaitTermination()
Can I take the same chain in the middle and create another grouping like
df2 = df \
.groupBy("myfield2") \
.agg(
expr("count(*) as cnt"),
min(struct(col("mycol.myfield").alias("mmm"), col("*"))).alias("minData")
) \
.select("cnt", "minData.*") \
.select(
col("...").alias("..."),
...
col("userId").alias("user_id")
and write it's ooutput into different place in parallel?
Where to call writeStream and awaitTermination?
Yes, you can branch a Kafka input stream into as many streaming queries as you like.
You need to consider the following:
query.awaitTermination is a blocking method, which means whatever code you are writing after this method will not be executed until this query gets terminated.
Each "branched" streaming query will run in parallel and is it important that you define a checkpoint location in each of their writeStream calls.
Overall, your code needs to have the following structure:
df = spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", config["kafka"]["bootstrap.servers"]) \
.option("subscribe", topic) \
.[...]
# note that I changed the variable name to "df1"
df1 = df \
.groupBy("myfield1") \
.[...]
df2 = df \
.groupBy("myfield2") \
.[...]
query1 = df1 \
.writeStream \
.outputMode("update") \
.option("checkpointLocation", "/tmp/checkpointLoc1") \
.foreachBatch(lambda df, epoch: write_data_frame(table_name, df1, epoch)) \
.start()
query2 = df2 \
.writeStream \
.outputMode("update") \
.option("checkpointLocation", "/tmp/checkpointLoc2") \
.foreachBatch(lambda df, epoch: write_data_frame(table_name, df2, epoch)) \
.start()
spark.streams.awaitAnyTermination
Just an additional remark: In the code you are showing, you are overwriting df, so the derivation of df2 might not get you the results as you were intended.
Am trying to truncate an Oracle table using pyspark using the below code
truncatesql = """ truncate table mytable """
mape=spark.read \
.format("jdbc") \
.option("url", DB_URL) \
.option("driver", "oracle.jdbc.driver.OracleDriver") \
.option("dbtable", truncatesql) \
.load()
but it keeps throwing java.sql.SQLSyntaxErrorException: ORA-00933: SQL command not properly ended how can I truncate a table using direct SQL query ?
Try by wrapping your query with an alias.
Example:
truncatesql = """(truncate table mytable)e"""
mape=spark.read \
.format("jdbc") \
.option("url", DB_URL) \
.option("driver", "oracle.jdbc.driver.OracleDriver") \
.option("dbtable", truncatesql) \
.load()