I'm using Spark to load data into a hive table through Pyspark, and when I load data from a path in Amazon S3, the original file is getting wiped from the Directory. The file is found, and is populating the table with data. I also tried to add the Local clause but that throws an error when looking for the file. When looking through the documentation it doesn't explicitly state that this is the intended behavior.
spark.sql("CREATE TABLE src (key INT, value STRING) STORED AS textfile")
spark.sql("LOAD DATA INPATH 's3://bucket/kv1.txt' OVERWRITE INTO TABLE src")
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I am trying to create an external hive table using spark. But facing below error:
using Create but with is expecting
Use of location implies that a created table via Spark it will be treated as an external table.
From the manual: https://docs.databricks.com/spark/latest/spark-sql/language-manual/create-table.html. You can also reference this: https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html
LOCATION
The created table uses the specified directory to store its data. This
clause automatically implies EXTERNAL.
More explicitly:
// Prepare a Parquet data directory
val dataDir = "/tmp/parquet_data"
spark.range(10).write.parquet(dataDir)
// Create a Hive external Parquet table
sql(s"CREATE EXTERNAL TABLE hive_bigints(id bigint) STORED AS PARQUET LOCATION '$dataDir'")
// The Hive external table should already have data
sql("SELECT * FROM hive_bigints").show()
Also, has nothing to do with pyspark.
If using spark dataframe writer, then the option "path" used below means unmanaged and thus external as well.
df.write.mode("OVERWRITE").option("path", unmanagedPath).saveAsTable("myTableUnmanaged")
I am simply trying to create a table in hive that is stored as a parquet file, and then transform the csv file that holds the data into a parquet file, and then load it into the hdfs directory to insert the values.below is my sequence that I am doing but to no avail:
First I created a table in Hive:
CREATE external table if not EXISTS db1.managed_table55 (dummy string)
stored as parquet
location '/hadoop/db1/managed_table55';
Then i loaded a parquet file into the above hdfs location using this spark:
df=spark.read.csv("/user/use_this.csv", header='true')
df.write.save('/hadoop/db1/managed_table55/test.parquet', format="parquet")
It loads but here is the output......all null values:
Here is the original values in the use_this.csv file that I transformed into a parquet file:
This is proof that the specified location created the table's folder(managed_table55) and the file(test.parquet):
Any ideas or suggestions as why this keeps happening? I know there's probably a minor tweak but I cant seem to identify it.
As you are writing parquet file to /hadoop/db1/managed_table55/test.parquet this location Try creating table in the same location and read the data from hive table.
Create Hive Table:
hive> CREATE external table if not EXISTS db1.managed_table55 (dummy string)
stored as parquet
location '/hadoop/db1/managed_table55/test.parquet';
Pyspark:
df=spark.read.csv("/user/use_this.csv", header='true')
df.write.save('/hadoop/db1/managed_table55/test.parquet', format="parquet")
I am trying to insert data into a Hive External table from Spark Sql.
I am created the hive external table through the following command
CREATE EXTERNAL TABLE tab1 ( col1 type,col2 type ,col3 type) CLUSTERED BY (col1,col2) SORTED BY (col1) INTO 8 BUCKETS STORED AS PARQUET
In my spark job , I have written the following code
Dataset df = session.read().option("header","true").csv(csvInput);
df.repartition(numBuckets, somecol)
.write()
.format("parquet")
.bucketBy(numBuckets,col1,col2)
.sortBy(col1)
.saveAsTable(hiveTableName);
Each time I am running this code I am getting the following exception
org.apache.spark.sql.AnalysisException: Table `tab1` already exists.;
at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:408)
at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:393)
at somepackage.Parquet_Read_WriteNew.writeToParquetHiveMetastore(Parquet_Read_WriteNew.java:100)
You should be specifying a save mode while saving the data in hive.
df.write.mode(SaveMode.Append)
.format("parquet")
.bucketBy(numBuckets,col1,col2)
.sortBy(col1)
.insertInto(hiveTableName);
Spark provides the following save modes:
Save Mode
ErrorIfExists: Throws an exception if the target already exists. If target doesn’t exist write the data out.
Append: If target already exists, append the data to it. If the data doesn’t exist write the data out.
Overwrite: If the target already exists, delete the target. Write the data out.
Ignore: If the target already exists, silently skip writing out. Otherwise write out the data.
You are using the saveAsTable API, which create the table into Hive. Since you have already created the hive table through command, the table tab1 already exists. so when Spark API trying to create it, it throws error saying table already exists, org.apache.spark.sql.AnalysisException: Tabletab1already exists.
Either drop the table and let spark API saveAsTable create the table itself.
Or use the API insertInto to insert into an existing hive table.
df.repartition(numBuckets, somecol)
.write()
.format("parquet")
.bucketBy(numBuckets,col1,col2)
.sortBy(col1)
.insertInto(hiveTableName);
I am creating a dataframe from existing hive table.Table is partitioned on date and site column.Now, when i am trying to overwrite the data in this same table after some computation with previous day data.It is successfully getting loaded.
But when i am trying to write final dataframe at S3 bucket. I am getting error saying file not found.Now the file it is mentioning is previous day file which is now overwritten.
If i write dataframe first and then overwrite table then its running fine.
For writing at S3 location , what it has to do with table partition file?
Below is the error and code.
java.io.FileNotFoundException: No such file or directory: s3://bucket_1/DM/web_fact_tbl/local_dt=2018-05-10/site_name=ABC/part-00000-882a6e29-eb6a-477c-8b88-6fe853956674.c000
fact_tbl = spark.table('db.web_fact_tbl')
fact_lkp = fact_tbl.filter(fact_tbl['local_dt']=='2018-05-10')
fact_join = fact_lkp.alias('a').join(fact_tbl.alias('b'),(col('a.id') == col('b.id')),"inner").select('a.*')
fact_final = fact_join.union(fact_tbl)
fact_final.coalesce(2).createOrReplaceTempView('cwf')
spark.sql('INSERT OVERWRITE TABLE dm.web_fact_tbl PARTITION (local_dt, site_name) \
SELECT * FROM cwf')
fact_final.write.csv('s3://bucket_1/yahoo')
Before last line fact_final is just a "lazy" dataframe object that contains definitions only. It does not contain any data. But it has pointer to exact data files, where data is stored actually.
When you try to perform actual operations (does not matter it's writing to S3, or executing query like fact_final.count()) you'll get the error as above. It looks like partition local_dt=2018-05-10 does not exists anymore (files/folder that sits behind it does not exists).
You can try to re-initialize dataframe once again, before final write (it's another lazy operation - all work is done in your case while you writing it on S3).
I have loaded a parquet table from HDFS into a DataFrame:
val df = spark.read.parquet("hdfs://user/zeppelin/my_table")
I now want to expose this table to Spark SQL but this must be a persitent table because I want to access it from a JDBC connection or other Spark Sessions.
Quick way could be to call df.write.saveAsTable method, but in this case it will materialize the contents of the DataFrame and create a pointer to the data in the Hive metastore, creating another copy of the data in HDFS.
I don't want to have two copies of the same data, so I would want create like an external table to point to existing data.
To create a Spark External table you must specify the "path" option of the DataFrameWriter. Something like this:
df.write.
option("path","hdfs://user/zeppelin/my_mytable").
saveAsTable("my_table")
The problem though, is that it will empty your hdfs path hdfs://user/zeppelin/my_mytable eliminating your existing files and will cause an org.apache.spark.SparkException: Job aborted.. This looks like a bug in Spark API...
Anyway, the workaround to this (tested in Spark 2.3) is to create an external table but from a Spark DDL. If your table have many columns creating the DDL could be a hassle. Fortunately, starting from Spark 2.0, you could call the DDL SHOW CREATE TABLE to let spark do the hard work. The problem is that you can actually run the SHOW CREATE TABLE in a persistent table.
If the table is pretty big, I recommend to get a sample of the table, persist it to another location, and then get the DDL. Something like this:
// Create a sample of the table
val df = spark.read.parquet("hdfs://user/zeppelin/my_table")
df.limit(1).write.
option("path", "/user/zeppelin/my_table_tmp").
saveAsTable("my_table_tmp")
// Now get the DDL, do not truncate output
spark.sql("SHOW CREATE TABLE my_table_tmp").show(1, false)
You are going to get a DDL like:
CREATE TABLE `my_table_tmp` (`ID` INT, `Descr` STRING)
USING parquet
OPTIONS (
`serialization.format` '1',
path 'hdfs:///user/zeppelin/my_table_tmp')
Which you would want to change to have the original name of the table and the path to the original data. You can now run the following to create the Spark External table pointing to your existing HDFS data:
spark.sql("""
CREATE TABLE `my_table` (`ID` INT, `Descr` STRING)
USING parquet
OPTIONS (
`serialization.format` '1',
path 'hdfs:///user/zeppelin/my_table')""")