I have generated some partitioned parquet data using Spark, and I'm wondering how to map it to an Impala table... Sadly, I haven't found any solution yet.
The schema of parquet is like :
{ key: long,
value: string,
date: long }
and I partitioned it with key and date, that gives me this kind of directories on my hdfs :
/data/key=1/date=20170101/files.parquet
/data/key=1/date=20170102/files.parquet
/data/key=2/date=20170101/files.parquet
/data/key=2/date=20170102/files.parquet
...
Do you know how I could tell Impala to create a table from this dataset with corresponding partitions (and without having to loop on each partition as I could have read) ? Is it possible ?
Thank you in advance
Assuming by schema of parquet , you meant the schema of the dataset and then using the columns to partition , you will have only the key column in the actual files.parquet files . Now you can proceed as follows
The solution is to use an impala external table .
create external table mytable (key BIGINT) partitioned by (value String ,
date BIGINT) stored as parquet location '....../data/'
Note that in above statement , you have to give path till the data folder
alter table mytable recover partitions'
refresh mytable;
The above 2 commands will automatically detect the partitions based on the schema of the table and get to know about the parquet files present in the sub directories.
Now , you can start querying the data .
Hope it helps
Related
I have a partitioned parquet at the following path:
/path/to/partitioned/parq/
with partitions like:
/path/to/partitioned/parq/part_date=2021_01_01_01_01_01
/path/to/partitioned/parq/part_date=2021_01_02_01_01_01
/path/to/partitioned/parq/part_date=2021_01_03_01_01_01
When I run a Spark SQL CREATE TABLE statement like:
CREATE TABLE IF NOT EXISTS
my_db.my_table
USING PARQUET
LOCATION '/path/to/partitioned/parq'
The partition column part_date shows up in my dataset, but DESCRIBE EXTENDED indicates there are no PARTITIONS. SHOW PARTITIONS my_db.my_table shows no partition data.
This seems to happen intermittently, like sometimes spark infers the partitions, other times it doesn't. This is causing issues downstream where we add a partition and try to MSCK REPAIR TABLE my_db.my_table and it says you can't run that on non-partitioned tables.
I see that if you DO declare schema, you can FORCE the PARTITIONED BY part of the clause but we do not have the luxury of a schema, just the files from underneath.
Why is spark intermittently unable to determine partition columns from a parquet in this shape?
Unfortunately with Hive you need to specify the schema, even if parquet obviously has this itself.
You need to add partition by clause to DDL.
Use ALTER table statement to add each partition separately with location.
I have external Hive Table which is filled by spark job and partitioned by(event_date date) now I have modified the spark code and added one extra column 'country'.In earlier written data country column will have null values as it is newly added. now I want to Alter 'partitioned by' clause as partition by(event_date date,country string) how can I achieve this.Thank you!!
Please try to alter the partition using below commnad-
ALTER TABLE table_name PARTITION part_spec SET LOCATION path
part_spec:
: (part_col_name1=val1, part_col_name2=val2, ...)
Try this databricks spark-sql language manual for alter command
I have a table in Databricks delta which is partitioned by transaction_date. I want to change the partition column to view_date. I tried to drop the table and then create it with a new partition column using PARTITIONED BY (view_date).
However my attempt failed since the actual files reside in S3 and even if I drop a hive table the partitions remain the same.
Is there any way to change the partition of an existing Delta table? Or the only solution will be to drop the actual data and reload it with a newly indicated partition column?
There's actually no need to drop tables or remove files. All you need to do is read the current table, overwrite the contents AND the schema, and change the partition column:
val input = spark.read.table("mytable")
input.write.format("delta")
.mode("overwrite")
.option("overwriteSchema", "true")
.partitionBy("colB") // different column
.saveAsTable("mytable")
UPDATE: There previously was a bug with time travel and changes in partitioning that has now been fixed.
As Silvio pointed out there is no need to drop the table. In fact the strongly recommended approach by databricks is to replace the table.
https://docs.databricks.com/sql/language-manual/sql-ref-syntax-ddl-create-table-using.html#parameters
in spark SQL, This can be done easily by
REPLACE TABLE <tablename>
USING DELTA
PARTITIONED BY (view_date)
AS
SELECT * FROM <tablename>
Modded example from:
https://docs.databricks.com/delta/best-practices.html#replace-the-content-or-schema-of-a-table
Python solution:
If you need more than one column in the partition
partitionBy(column, column_2, ...)
def change_partition_of(table_name, column):
df = spark.read.table(tn)
df.write.format("delta").mode("overwrite").option("overwriteSchema", "true").partitionBy(column).saveAsTable(table_name)
change_partition_of("i.love_python", "column_a")
I'm having an issue writing a Hive table from Spark. The following code works just fine; I can write the table (which defaults to the Parquet format) and read it back in Hive:
df.write.mode('overwrite').saveAsTable("db.table")
hive> describe table;
OK
val string
Time taken: 0.021 seconds, Fetched: 1 row(s)
However, if I specify the format should be csv:
df.write.mode('overwrite').format('csv').saveAsTable("db.table")
then I can save the table, but Hive doesn't recognize the schema:
hive> describe table;
OK
col array<string> from deserializer
Time taken: 0.02 seconds, Fetched: 1 row(s)
It's also worth noting that I can create a Hive table manually and then insertInto it:
spark.sql("create table db.table(val string)")
df.select('val').write.mode("overwrite").insertInto("db.table")
Doing so, Hive seems to recognize the schema. But that's clunky and I can't figure a way to automate the schema string anyway.
That is because Hive SerDe do not support csv by default.
If you insist on using csv format, creating table as below:
CREATE TABLE my_table(a string, b string, ...)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.OpenCSVSerde'
WITH SERDEPROPERTIES (
"separatorChar" = "\t",
"quoteChar" = "'",
"escapeChar" = "\\"
)
STORED AS TEXTFILE;
And insert data through df.write.insertInto
For more info:
https://cwiki.apache.org/confluence/display/Hive/CSV+Serde
You are creating a table with text format and trying to insert CSV data into it, which may run in to problems. So as suggested in the answer by Zhang Tong, create the hive table using hive OpenCSVSerde.
After that, if you are more comfortable with Hive query language than dataframes, you can try this.
df.registerTempTable("temp")
spark.sql("insert overwrite db.table select * from temp")
This happens because HiveSerde is different for csv than what is used by Spark. Hive by default use TEXTFORMAT and the delimiter has to be specified while creating the table.
One Option is to use the insertInto API instead of saveAsTable while writing from spark. While using insertInto, Spark writes the contents of the Dataframe to the specified table. But it requires the schema of the dataframe to be same as the schema of the table. Position of the columns is important here as it ignores the column names.
Seq((5, 6)).toDF("a", "b").write.insertInto("t1")
The Hive table was created using 4 partitions.
CREATE TABLE IF NOT EXISTS hourlysuspect ( cells int, sms_in int) partitioned by (traffic_date_hour string) stored as ORC into 4 buckets
The following lines in the spark code insert data into this table
hourlies.write.partitionBy("traffic_date_hour").insertInto("hourly_suspect")
and in the spark-defaults.conf, the number of parallel processes is 128
spark.default.parallelism=128
The problem is that when the inserts happen in the hive table, it has 128 partitions instead of 4 buckets.
The defaultParallelism cannot be reduced to 4 as that leads to a very very slow system. Also, I have tried the DataFrame.coalesce method but that makes the inserts too slow.
Is there any other way to force the number of buckets to be 4 when the data is inserted into the table?
As of today {spark 2.2.0} Spark does not support writing to bucketed hive tables natively using spark-sql. While creating the bucketed table, there should be a clusteredBy clause on one of the columns form the table schema. I don't see that in the specified CreateTable statement. Assuming, that it does exist and you know the clustering column, you could add the
.bucketBy([colName])
API while using DataFrameWriter API.
More details for Spark2.0+: [Link] (https://spark.apache.org/docs/2.0.0/api/java/org/apache/spark/sql/DataFrameWriter.html)