I am following the instructions found here to connect my spark program to read data from Cassandra. Here is how I have configured spark:
val configBuilder = SparkSession.builder
.config("spark.sql.extensions", "com.datastax.spark.connector.CassandraSparkExtensions")
.config("spark.cassandra.connection.host", cassandraUrl)
.config("spark.cassandra.connection.port", 9042)
.config("spark.sql.catalog.myCatalogName", "com.datastax.spark.connector.datasource.CassandraCatalog")
According to the documentation, once this is done I should be able to query Cassandra like this:
spark.sql("select * from myCatalogName.myKeyspace.myTable where myPartitionKey = something")
however when I do so I get the following error message:
mismatched input '.' expecting <EOF>(line 1, pos 43)
== SQL ==
select * from myCatalog.myKeyspace.myTable where myPartitionKey = something
----------------------------------^^^
When I try in the following format I am successful at retrieving entries from Cassandra:
val frame = spark
.read
.format("org.apache.spark.sql.cassandra")
.options(Map("keyspace" -> "myKeyspace", "table" -> "myTable"))
.load()
.filter(col("timestamp") > startDate && col("timestamp") < endDate)
However this query requires a full table scan to be performed. The table contains a few million entries and I would prefer to avail myself of the predicate Pushdown functionality, which it would seem is only available via the SQL API.
I am using spark-core_2.11:2.4.3, spark-cassandra-connector_2.11:2.5.0 and Cassandra 3.11.6
Thanks!
The Catalogs API is available only in SCC version 3.0 that is not released yet. It will be released with Spark 3.0 release, so it isn't available in the SCC 2.5.0. So for 2.5.0 you need to register your table explicitly, with create or replace temporary view..., as described in docs:
spark.sql("""CREATE TEMPORARY VIEW myTable
USING org.apache.spark.sql.cassandra
OPTIONS (
table "myTable",
keyspace "myKeyspace",
pushdown "true")""")
Regarding the pushdowns (they work the same for all Dataframe APIs, SQL, Scala, Python, ...) - such filtering will happen when your timestamp is the first clustering column. And even in that case, the typical problem is that you may specify startDate and endDate as strings, not timestamp. You can check by executing frame.explain, and checking that predicate is pushed down - it should have * marker near predicate name.
For example,
val data = spark.read.cassandraFormat("sdtest", "test").load()
val filtered = data.filter("ts >= cast('2019-03-10T14:41:34.373+0000' as timestamp) AND ts <= cast('2019-03-10T19:01:56.316+0000' as timestamp)")
val not_filtered = data.filter("ts >= '2019-03-10T14:41:34.373+0000' AND ts <= '2019-03-10T19:01:56.316+0000'")
the first filter expression will push predicate down, while 2nd (not_filtered) will require a full scan.
I have a hive table created on top of s3 DATA in parquet format and partitioned by one column named eventdate.
1) When using HIVE QUERY, it returns data for a column named "headertime" which is in the schema of BOTH the table and the file.
select headertime from dbName.test_bug where eventdate=20180510 limit 10
2) FROM a scala NOTEBOOK , when directly loading a file from a particular partition that also works,
val session = org.apache.spark.sql.SparkSession.builder
.appName("searchRequests")
.enableHiveSupport()
.getOrCreate;
val searchRequest = session.sqlContext.read.parquet("s3n://bucketName/module/search_request/eventDate=20180510")
searchRequest.createOrReplaceTempView("SearchRequest")
val exploreDF = session.sql("select headertime from SearchRequest where SearchRequestHeaderDate='2018-05-10' limit 100")
exploreDF.show(20)
this also displays the values for the column "headertime"
3) But, when using spark sql to query directly the HIVE table as below,
val exploreDF = session.sql("select headertime from tier3_vsreenivasan.test_bug where eventdate=20180510 limit 100")
exploreDF.show(20)
it keeps returning null always.
I opened the parquet file and see that the column headertime is present with values, but not sure why spark SQL is not able to read the values for that column.
it will be helpful if someone can point out from where the spark SQL gets the schema? I was expecting it to behave similar to the HIVE QUERY
Currently I'm trying to filter a Hive table by the latest date_processed.
The table is partitioned by.
System
date_processed
Region
The only way I've managed to filter it, is by doing a join query:
query = "select * from contracts_table as a join (select (max(date_processed) as maximum from contract_table as b) on a.date_processed = b.maximum"
This way is really time consuming, as I have to do the same procedure for 25 tables.
Any one Knows a way to read directly the latest loaded partition of a table in Spark <1.6
This is the method I'm using to read.
public static DataFrame loadAndFilter (String query)
{
return SparkContextSingleton.getHiveContext().sql(+query);
}
Many thanks!
Dataframe with all table partitions can be received by:
val partitionsDF = hiveContext.sql("show partitions TABLE_NAME")
Values can be parsed, for get max value.
My cassandra CF has date and id as partition Key .
while querying I only know the date , so I loop over range of id's .
My question revolves around how the connector executes the following code.
SparkDriver code looks like -
SparkConf conf = new SparkConf().setAppName("DemoApp")
.conf.setMaster("local[*]")
.set("spark.cassandra.connection.host", "10.*.*.*")
.set("spark.cassandra.connection.port", "*");
JavaSparkContext sc = new JavaSparkContext(conf);
SparkContextJavaFunctions javaFunctions = CassandraJavaUtil.javaFunctions(sc);
String date = "23012017";
for(String id : idlist) {
JavaRDD<CassandraRow> cassandraRowsRDD =
javaFunctions.cassandraTable("datakeyspace", "sample2")
.where("date = ?",date)
.where("id = ? ", id)
.select("data");
cassandraRowsRDDList.add(cassandraRowsRDD);
}
List<CassandraRow> collectAllRows = new ArrayList<CassandraRow>();
for(JavaRDD<CassandraRow> rdd : cassandraRowsRDDList){
//do transformations
collectAllRows.addAll(rdd.collect());
}
1) First of all I wanted to ask if I loop over the idlist ,say idlist has 1000 elements which might be increasing ever , will this be efficient ? how each select query be distributed in the cluster ?Especially how Cassandra DB connections will be maintained ?
2) In my driver program After looping over I am putting All the rows in List , and then apply transformations to each row and filter out the duplicates . Will this also be distributed by spark on the cluster or will this take place at driver's side .
Kindly help .!
There is a better way of doing this provided by spark cassandra connector.
you can create a rdd of (date,id) and then call joinWithCassandraTable function on columns date and id. Connector do it smartly all the data will be fetched by the workers only and that too without shuffle that is each worker will fetch the data only for the date and id it is having.
I want to overwrite specific partitions instead of all in spark. I am trying the following command:
df.write.orc('maprfs:///hdfs-base-path','overwrite',partitionBy='col4')
where df is dataframe having the incremental data to be overwritten.
hdfs-base-path contains the master data.
When I try the above command, it deletes all the partitions, and inserts those present in df at the hdfs path.
What my requirement is to overwrite only those partitions present in df at the specified hdfs path. Can someone please help me in this?
Finally! This is now a feature in Spark 2.3.0:
SPARK-20236
To use it, you need to set the spark.sql.sources.partitionOverwriteMode setting to dynamic, the dataset needs to be partitioned, and the write mode overwrite. Example:
spark.conf.set("spark.sql.sources.partitionOverwriteMode","dynamic")
data.write.mode("overwrite").insertInto("partitioned_table")
I recommend doing a repartition based on your partition column before writing, so you won't end up with 400 files per folder.
Before Spark 2.3.0, the best solution would be to launch SQL statements to delete those partitions and then write them with mode append.
This is a common problem. The only solution with Spark up to 2.0 is to write directly into the partition directory, e.g.,
df.write.mode(SaveMode.Overwrite).save("/root/path/to/data/partition_col=value")
If you are using Spark prior to 2.0, you'll need to stop Spark from emitting metadata files (because they will break automatic partition discovery) using:
sc.hadoopConfiguration.set("parquet.enable.summary-metadata", "false")
If you are using Spark prior to 1.6.2, you will also need to delete the _SUCCESS file in /root/path/to/data/partition_col=value or its presence will break automatic partition discovery. (I strongly recommend using 1.6.2 or later.)
You can get a few more details about how to manage large partitioned tables from my Spark Summit talk on Bulletproof Jobs.
spark.conf.set("spark.sql.sources.partitionOverwriteMode","dynamic")
data.toDF().write.mode("overwrite").format("parquet").partitionBy("date", "name").save("s3://path/to/somewhere")
This works for me on AWS Glue ETL jobs (Glue 1.0 - Spark 2.4 - Python 2)
Adding 'overwrite=True' parameter in the insertInto statement solves this:
hiveContext.setConf("hive.exec.dynamic.partition", "true")
hiveContext.setConf("hive.exec.dynamic.partition.mode", "nonstrict")
df.write.mode("overwrite").insertInto("database_name.partioned_table", overwrite=True)
By default overwrite=False. Changing it to True allows us to overwrite specific partitions contained in df and in the partioned_table. This helps us avoid overwriting the entire contents of the partioned_table with df.
Using Spark 1.6...
The HiveContext can simplify this process greatly. The key is that you must create the table in Hive first using a CREATE EXTERNAL TABLE statement with partitioning defined. For example:
# Hive SQL
CREATE EXTERNAL TABLE test
(name STRING)
PARTITIONED BY
(age INT)
STORED AS PARQUET
LOCATION 'hdfs:///tmp/tables/test'
From here, let's say you have a Dataframe with new records in it for a specific partition (or multiple partitions). You can use a HiveContext SQL statement to perform an INSERT OVERWRITE using this Dataframe, which will overwrite the table for only the partitions contained in the Dataframe:
# PySpark
hiveContext = HiveContext(sc)
update_dataframe.registerTempTable('update_dataframe')
hiveContext.sql("""INSERT OVERWRITE TABLE test PARTITION (age)
SELECT name, age
FROM update_dataframe""")
Note: update_dataframe in this example has a schema that matches that of the target test table.
One easy mistake to make with this approach is to skip the CREATE EXTERNAL TABLE step in Hive and just make the table using the Dataframe API's write methods. For Parquet-based tables in particular, the table will not be defined appropriately to support Hive's INSERT OVERWRITE... PARTITION function.
Hope this helps.
Tested this on Spark 2.3.1 with Scala.
Most of the answers above are writing to a Hive table. However, I wanted to write directly to disk, which has an external hive table on top of this folder.
First the required configuration
val sparkSession: SparkSession = SparkSession
.builder
.enableHiveSupport()
.config("spark.sql.sources.partitionOverwriteMode", "dynamic") // Required for overwriting ONLY the required partitioned folders, and not the entire root folder
.appName("spark_write_to_dynamic_partition_folders")
Usage here:
DataFrame
.write
.format("<required file format>")
.partitionBy("<partitioned column name>")
.mode(SaveMode.Overwrite) // This is required.
.save(s"<path_to_root_folder>")
I tried below approach to overwrite particular partition in HIVE table.
### load Data and check records
raw_df = spark.table("test.original")
raw_df.count()
lets say this table is partitioned based on column : **c_birth_year** and we would like to update the partition for year less than 1925
### Check data in few partitions.
sample = raw_df.filter(col("c_birth_year") <= 1925).select("c_customer_sk", "c_preferred_cust_flag")
print "Number of records: ", sample.count()
sample.show()
### Back-up the partitions before deletion
raw_df.filter(col("c_birth_year") <= 1925).write.saveAsTable("test.original_bkp", mode = "overwrite")
### UDF : To delete particular partition.
def delete_part(table, part):
qry = "ALTER TABLE " + table + " DROP IF EXISTS PARTITION (c_birth_year = " + str(part) + ")"
spark.sql(qry)
### Delete partitions
part_df = raw_df.filter(col("c_birth_year") <= 1925).select("c_birth_year").distinct()
part_list = part_df.rdd.map(lambda x : x[0]).collect()
table = "test.original"
for p in part_list:
delete_part(table, p)
### Do the required Changes to the columns in partitions
df = spark.table("test.original_bkp")
newdf = df.withColumn("c_preferred_cust_flag", lit("Y"))
newdf.select("c_customer_sk", "c_preferred_cust_flag").show()
### Write the Partitions back to Original table
newdf.write.insertInto("test.original")
### Verify data in Original table
orginial.filter(col("c_birth_year") <= 1925).select("c_customer_sk", "c_preferred_cust_flag").show()
Hope it helps.
Regards,
Neeraj
As jatin Wrote you can delete paritions from hive and from path and then append data
Since I was wasting too much time with it I added the following example for other spark users.
I used Scala with spark 2.2.1
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.Path
import org.apache.spark.SparkConf
import org.apache.spark.sql.{Column, DataFrame, SaveMode, SparkSession}
case class DataExample(partition1: Int, partition2: String, someTest: String, id: Int)
object StackOverflowExample extends App {
//Prepare spark & Data
val sparkConf = new SparkConf()
sparkConf.setMaster(s"local[2]")
val spark = SparkSession.builder().config(sparkConf).getOrCreate()
val tableName = "my_table"
val partitions1 = List(1, 2)
val partitions2 = List("e1", "e2")
val partitionColumns = List("partition1", "partition2")
val myTablePath = "/tmp/some_example"
val someText = List("text1", "text2")
val ids = (0 until 5).toList
val listData = partitions1.flatMap(p1 => {
partitions2.flatMap(p2 => {
someText.flatMap(
text => {
ids.map(
id => DataExample(p1, p2, text, id)
)
}
)
}
)
})
val asDataFrame = spark.createDataFrame(listData)
//Delete path function
def deletePath(path: String, recursive: Boolean): Unit = {
val p = new Path(path)
val fs = p.getFileSystem(new Configuration())
fs.delete(p, recursive)
}
def tableOverwrite(df: DataFrame, partitions: List[String], path: String): Unit = {
if (spark.catalog.tableExists(tableName)) {
//clean partitions
val asColumns = partitions.map(c => new Column(c))
val relevantPartitions = df.select(asColumns: _*).distinct().collect()
val partitionToRemove = relevantPartitions.map(row => {
val fields = row.schema.fields
s"ALTER TABLE ${tableName} DROP IF EXISTS PARTITION " +
s"${fields.map(field => s"${field.name}='${row.getAs(field.name)}'").mkString("(", ",", ")")} PURGE"
})
val cleanFolders = relevantPartitions.map(partition => {
val fields = partition.schema.fields
path + fields.map(f => s"${f.name}=${partition.getAs(f.name)}").mkString("/")
})
println(s"Going to clean ${partitionToRemove.size} partitions")
partitionToRemove.foreach(partition => spark.sqlContext.sql(partition))
cleanFolders.foreach(partition => deletePath(partition, true))
}
asDataFrame.write
.options(Map("path" -> myTablePath))
.mode(SaveMode.Append)
.partitionBy(partitionColumns: _*)
.saveAsTable(tableName)
}
//Now test
tableOverwrite(asDataFrame, partitionColumns, tableName)
spark.sqlContext.sql(s"select * from $tableName").show(1000)
tableOverwrite(asDataFrame, partitionColumns, tableName)
import spark.implicits._
val asLocalSet = spark.sqlContext.sql(s"select * from $tableName").as[DataExample].collect().toSet
if (asLocalSet == listData.toSet) {
println("Overwrite is working !!!")
}
}
If you use DataFrame, possibly you want to use Hive table over data.
In this case you need just call method
df.write.mode(SaveMode.Overwrite).partitionBy("partition_col").insertInto(table_name)
It'll overwrite partitions that DataFrame contains.
There's not necessity to specify format (orc), because Spark will use Hive table format.
It works fine in Spark version 1.6
Instead of writing to the target table directly, i would suggest you create a temporary table like the target table and insert your data there.
CREATE TABLE tmpTbl LIKE trgtTbl LOCATION '<tmpLocation';
Once the table is created, you would write your data to the tmpLocation
df.write.mode("overwrite").partitionBy("p_col").orc(tmpLocation)
Then you would recover the table partition paths by executing:
MSCK REPAIR TABLE tmpTbl;
Get the partition paths by querying the Hive metadata like:
SHOW PARTITONS tmpTbl;
Delete these partitions from the trgtTbl and move the directories from tmpTbl to trgtTbl
I would suggest you doing clean-up and then writing new partitions with Append mode:
import scala.sys.process._
def deletePath(path: String): Unit = {
s"hdfs dfs -rm -r -skipTrash $path".!
}
df.select(partitionColumn).distinct.collect().foreach(p => {
val partition = p.getAs[String](partitionColumn)
deletePath(s"$path/$partitionColumn=$partition")
})
df.write.partitionBy(partitionColumn).mode(SaveMode.Append).orc(path)
This will delete only new partitions. After writing data run this command if you need to update metastore:
sparkSession.sql(s"MSCK REPAIR TABLE $db.$table")
Note: deletePath assumes that hfds command is available on your system.
My solution implies overwriting each specific partition starting from a spark dataframe. It skips the dropping partition part. I'm using pyspark>=3 and I'm writing on AWS s3:
def write_df_on_s3(df, s3_path, field, mode):
# get the list of unique field values
list_partitions = [x.asDict()[field] for x in df.select(field).distinct().collect()]
df_repartitioned = df.repartition(1,field)
for p in list_partitions:
# create dataframes by partition and send it to s3
df_to_send = df_repartitioned.where("{}='{}'".format(field,p))
df_to_send.write.mode(mode).parquet(s3_path+"/"+field+"={}/".format(p))
The arguments of this simple function are the df, the s3_path, the partition field, and the mode (overwrite or append). The first part gets the unique field values: it means that if I'm partitioning the df by daily, I get a list of all the dailies in the df. Then I'm repartition the df. Finally, I'm selecting the repartitioned df by each daily and I'm writing it on its specific partition path.
You can change the repartition integer by your needs.
You could do something like this to make the job reentrant (idempotent):
(tried this on spark 2.2)
# drop the partition
drop_query = "ALTER TABLE table_name DROP IF EXISTS PARTITION (partition_col='{val}')".format(val=target_partition)
print drop_query
spark.sql(drop_query)
# delete directory
dbutils.fs.rm(<partition_directoy>,recurse=True)
# Load the partition
df.write\
.partitionBy("partition_col")\
.saveAsTable(table_name, format = "parquet", mode = "append", path = <path to parquet>)
For >= Spark 2.3.0 :
spark.conf.set("spark.sql.sources.partitionOverwriteMode","dynamic")
data.write.insertInto("partitioned_table", overwrite=True)