Given some table manipulation – create table with 2 rows and columns, add 3rd column and insert third row with 3 values
CREATE TABLE concat_test(
one string,
two string
)
STORED AS ORC;
INSERT INTO TABLE concat_test VALUES (1,1), (2,2);
ALTER TABLE concat_test ADD COLUMNS (three string);
INSERT INTO TABLE concat_test VALUES (3,3,3);
alter table concat_test concatenate;
I'm having an exception Caused by: java.lang.ArrayIndexOutOfBoundsException: 3 when I try reading it with Spark
spark.sql("select * from concat_test").collect()
It is obviously connected with columns number. I'm further investigating problem in orc. I didn't find quick fix for such partitions nor the bug described elsewhere. Is there one?
Could anyone try this on the latest hadoop versions? Does the bug exist?
Hive 1.2.1, Spark 2.3.2
UPD. I myself fixed my tables via Hive. Hive queries do work after this manipulation so I created copy tables and did select-insert of the old data to them.
I have totally run into this issue before!
This is a known issue.
Hive only does schema on read, so there is no reason it should detect this as an issue and will happily let you define any definition you want. And the data underlying the table does NOT get updated when you change the definition of the hive table. Generally I have fixed the issue by fixing the underlying ORC files to meet the hive definition. You could read the ORC files directly as that issue has been fixed now as a work around.
Here's a work around if you know that the underlying orc files aren't in the correct format and want to correct the format.
val s = Seq(("apple","apples"),("car","cars")) // create data
val t = Seq(("apple",12),("apples", 50),("car",5),("cars",40))// create data
val df1 = sc.parallelize(t).toDF("Sub_Cat", "Count")
val df2 = sc.parallelize(s).toDF("Main_Cat","Sub_Cat")
df1.write.format("orc").save("category_count")
df2.write.format("orc").save("categories")
val schema = StructType( Array( StructField("Main_Cat", StringType, nullable = true), StructField("Sub_Cat", StringType, nullable = true),StructField("Count", IntegerType, nullable = true)) )
val CorrectedSchema = spark.read.schema(schema).org("category_count")
CorrectedSchema.show()
This helps to correct Schema into the format you intend. If you trust the hive schema you can use this cheat to get the schema.(and reduce the typing)
val schema = spark.sql("select * from concat_test limit 0").schema
I am working on a Spring Java Project and integrating Apache spark and cassandra using Datastax connector.
I have autowired sparkSession and the below lines of code seems to work.
Map<String, String> configMap = new HashMap<>();
configMap.put("keyspace", "key1");
configMap.put("table", tableName.toLowerCase());
Dataset<Row> ds = sparkSession.sqlContext().read().format("org.apache.spark.sql.cassandra").options(configMap)
.load();
ds.show();
In the above step I am loading Datasets and in below step I am doing filtration of datetime field .
String s1 = "2020-06-23 18:51:41";
String s2 = "2020-06-23 18:52:21";
Timestamp from = Timestamp.valueOf(s1);
Timestamp to = Timestamp.valueOf(s2);
ds = ds.filter(df.col("datetime").between(from, to));
Is it possible to apply this filter condition during load itself.If so can someone suggest me how to do this?
Thanks in advance.
You don't have to do anything explicitly here, spark-cassandra-connector has predicate pushdown, so your filtering condition would be applied during the data selection.
Source: https://github.com/datastax/spark-cassandra-connector/blob/master/doc/14_data_frames.md
The connector will automatically pushdown all valid predicates to Cassandra. The Datasource will also automatically only select columns from Cassandra which are required to complete the query. This can be monitored with the explain command.
This filter will be effectively pushed down only if your column on which you're doing filtering is the first clustering column. As Rayan pointed, we can use the explain command on the dataset to check that predicates pushdown happened - the corresponding predicates should have the * characters near them, like this:
val dcf3 = dc.filter("event_time >= cast('2019-03-10T14:41:34.373+0000' as timestamp)
AND event_time <= cast('2019-03-10T19:01:56.316+0000' as timestamp)")
// dcf3.explain
// == Physical Plan ==
// *Scan org.apache.spark.sql.cassandra.CassandraSourceRelation [uuid#21,event_time#22,id#23L,value#24]
// PushedFilters: [ *GreaterThanOrEqual(event_time,2019-03-10 14:41:34.373), *LessThanOrE...,
// ReadSchema: struct<uuid:string,event_time:timestamp,id:bigint,value...
if predicate won't be pushed, we would see an additional step after scan when the filtering happens on the Spark level.
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 am trying to convert the Column in the Dataset from varchar to UUID using the custom datatype in Spark SQL. But i see the conversion not happening. Please let me know if i am missing anything here.
val secdf = sc.parallelize( Array(("85d8b889-c793-4f23-93e9-ea18db640039","Revenue"), ("85d8b889-c793-4f23-93e9-ea18db640038","Income:123213"))).toDF("id", "report")
val metadataBuilder = new MetadataBuilder()
metadataBuilder.putString("database.column.type", "uuid")
metadataBuilder.putLong("jdbc.type", java.sql.Types.OTHER)
val metadata = metadataBuilder.build()
val secReportDF = secdf.withColumn("id", col("id").as("id", metadata))
i did the work around as we are not able to cast to UUID in Spark SQL, i have added the property in the Postgres JDBC client as stringtype=unspecified which solved my issue in Inserting UUID through Spark JDBC
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)