I have a sql statement query which is doing a group by on many fields. The tables that it uses is also big (4TB in size). I'm registering the table as a temp table. However I don't know whether the table gets cached or not when I'm registering it as a temp table? I also don't know whether it is more performant if I convert my query into Scala function (e.g. df.groupby().aggr()...) rather than having it as a sql statement. Any help on that?
SQL is most likely going to be the fastest by far Databricks blog
Did you try to partition/repartition your dataframe as well to see whether it improves the performance?
Regarding registerTempTable: it only registers the table within a spark context. You can check with the UI.
val test = List((1,2,3),(4,5,6)).toDF("bla","blb","blc")
test.createOrReplaceTempView("test")
test.show()
Storage is blank
vs
val test = List((1,2,3),(4,5,6)).toDF("bla","blb","blc")
test.createOrReplaceTempView("test").cache()
test.show()
by the way registerTempTable is deprecated in Spark 2.0 and has been replaced by
createOrReplaceTempView
I have a sql statement query which is doing a group by on many fields. The tables that it uses is also big (4TB in size). I'm registering the table as a temp table. However I don't know whether the table gets cached or not when I'm registering it as a temp table?
The registerTempTabele or createOrReplaceTempView doesn't cache the data into memory or disc itself unless you use cache() function.
I also don't know whether it is more performant if I convert my query into Scala function (e.g. df.groupby().aggr()...) rather than having it as a sql statement. Any help on that?
Keep in mind the sql terms in sql query ultimately call the function inside. so whether you use sql query terms or functions available in code it doesn't matter. that is same thing.
Related
I've seen follow code and i think that it is a wrong way to cache tempview in Spark. What do you think?
spark.sql(
s"""
|...
""".stripMargin).createOrReplaceTempView(s"temp_view")
spark.table(s"temp_view").cache()
For my opinion, this code caches DataFrame that I create by spark.table("temp_view"), but not original temp view.
Am I right?
Imo yes, you are caching what you read from this table, but for example if in next line you are going to read it again you will end up with second scan
I think that maybe you can try to use cache table within your sql
https://spark.apache.org/docs/latest/sql-ref-syntax-aux-cache-cache-table.html
CACHE TABLE statement caches contents of a table or output of a query
with the given storage level. If a query is cached, then a temp view
will be created for this query. This reduces scanning of the original
files in future queries.
For me its seems promising
I think the caching in your example will actually work. Spark does not cache instances of DataFrame. Instead, it uses logical plans as the cache key, and the view is transparent for that purpose. For example, here's the code I've just tried using some local table I have
val df = spark.table("mart.dim_region")
df.createOrReplaceTempView("dim_region")
spark.table("dim_region").cache()
Even though cache is applied to view, if I repeatedly invoke df.show, the execution plan contains InMemoryTableScan - which is precisely the effect of caching.
I'm working with two environments in Azure: Databricks and SQL Database. I'm working with a function that generate a dataframe that it's going to be used to overwrite the table that is stored in the SQL Database. I have many problems because the df.write.jdbc(mode = 'overwrite') only drops the table and, I'm guessing, my user didn't have the right permissions to created again (I've already seen for DML and DDL permission that I need to do that). In resume, my functions only drops the table but without recreating again.
We discuss about what could be the problem and we conclude that maybe the best thing that I can do is truncate the table and re-add the new data there. I'm trying to find how to truncate the table, I tried these two approaches but I can't find more information related to that:
df.write.jdbc()
&
spark.read.jdbc()
Can you help me with these? The overwrite doesn't work (maybe I don't have the adequate permissions) and I can't figure out how to truncate that table using a jdbc.
It's in the Spark documentation - you need to add the truncate when writing:
df.write.mode("overwrite").option("truncate", "true")....save()
Also, if you have a lot of data, then maybe it's better to use Microsoft's Spark connector for SQL Server - it has some performance optimizations that should allow to write faster.
You can create stored procedure for truncating or dropping in SQL Server and call that stored procedure in databricks using ODBC connection.
I want to apply SCD1 and SCD2 using PySpark in HiveContext. In my approach, I am reading incremental data and target table. After reading, I am joining them for upsert approach. I am doing registerTempTable on all the source dataframes. I am trying to write final dataset into target table and I am facing the issue that Insert overwrite is not possible in the table it is read from.
Please suggest some solution for this. I do not want to write intermediate data into a physical table and read it again.
Is there any property or way to store the final data set without keeping the dependency on the table it is read from. This way, It might be possible to overwrite the table.
Please suggest.
You should never overwrite a table from which you are reading. It can result in anything between data corruption and complete data loss in case of failure.
It is also important to point out that correctly implemented SCD2 shouldn't never overwrite a whole table and can be implemented as a (mostly) append operation. As far as I am aware SCD1 cannot be efficiently implemented without mutable storage, therefore is not a good fit for Spark.
I was going through the documentation of spark and a thought clicked to me when I was checking one property there.
As my table was parquet, I used hive meta store to read the data by setting this property to false.
hiveContext.conf("spark.sql.hive.convertMetastoreParquet","false")
This solution is working fine for me.
I am looking for examples of UDFs in Cassandra. So far, the examples I have found are doing some computation and returning the result. I am looking for how to perform multiple database queries, store the result of some query in an intermediate table or variable. Then use the value of that variable to perform another query. Not sure how to do that. Can anyone help ?
PS: I am using the Node.js driver.
It's not possible with Cassandra alone. You can do it together with Spark and push the intermediate tables back to the database.
I'm evaluating spark-cassandra-connector and i'm struggling trying to get a range query on partition key to work.
According to the connector's documentation it seems that's possible to make server-side filtering on partition key using equality or IN operator, but unfortunately, my partition key is a timestamp, so I can not use it.
So I tried using Spark SQL with the following query ('timestamp' is the partition key):
select * from datastore.data where timestamp >= '2013-01-01T00:00:00.000Z' and timestamp < '2013-12-31T00:00:00.000Z'
Although the job spawns 200 tasks, the query is not returning any data.
Also I can assure that there is data to be returned since running the query on cqlsh (doing the appropriate conversion using 'token' function) DOES return data.
I'm using spark 1.1.0 with standalone mode. Cassandra is 2.1.2 and connector version is 'b1.1' branch. Cassandra driver is DataStax 'master' branch.
Cassandra cluster is overlaid on spark cluster with 3 servers with replication factor of 1.
Here is the job's full log
Any clue anyone?
Update: When trying to do server-side filtering based on the partition key (using CassandraRDD.where method) I get the following exception:
Exception in thread "main" java.lang.UnsupportedOperationException: Range predicates on partition key columns (here: timestamp) are not supported in where. Use filter instead.
But unfortunately I don't know what "filter" is...
i think the CassandraRDD error is telling that the query that you are trying to do is not allowed in Cassandra and you have to load all the table in a CassandraRDD and then make a spark filter operation over this CassandraRDD.
So your code (in scala) should something like this:
val cassRDD= sc.cassandraTable("keyspace name", "table name").filter(row=> row.getDate("timestamp")>=DateFormat('2013-01-01T00:00:00.000Z')&&row.getDate("timestamp") < DateFormat('2013-12-31T00:00:00.000Z'))
If you are interested in making this type of queries you might have to take a look to others Cassandra connectors, like the one developed by Stratio
You have several options to get the solution you are looking for.
The most powerful one would be to use Lucene indexes integrated with Cassandra by Stratio, which allows you to search by any indexed field in the server side. Your writing time will be increased but, on the other hand, you will be able to query any time range. You can find further information about Lucene indexes in Cassandra here. This extended version of Cassandra is fully integrated into the deep-spark project so you can take all the advantages of the Lucene indexes in Cassandra through it. I would recommend you to use Lucene indexes when you are executing a restricted query that retrieves a small-medium result set, if you are going to retrieve a big piece of your data set, you should use the third option underneath.
Another approach, depending on how your application works, might be to truncate your timestamp field so you can look for it using an IN operator. The problem is, as far as I know, you can't use the spark-cassandra-connector for that, you should use the direct Cassandra driver which is not integrated with Spark, or you can have a look at the deep-spark project where a new feature allowing this is about to be released very soon. Your query would look something like this:
select * from datastore.data where timestamp IN ('2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', ... , '2013-12-31')
, but, as I said before, I don't know if it fits to your needs since you might not be able to truncate your data and group it by date/time.
The last option you have, but the less efficient, is to bring the full data set to your spark cluster and apply a filter on the RDD.
Disclaimer: I work for Stratio :-) Don't hesitate on contacting us if you need any help.
I hope it helps!