I have a api endpoint written by sparksql with the following sample code. Every time api accept a request it will run sparkSession.sql(sql_to_hive) which would create a single file in HDFS. Is there any way to do insert by appending data to existing file in HDFS ? Thanks.
sqlContext = SQLContext(sparkSession.sparkContext)
df = sqlContext.createDataFrame(ziped_tuple_list, schema=schema)
df.registerTempTable('TMP_TABLE')
sql_to_hive = 'insert into log.%(table_name)s partition%(partition)s select %(title_str)s from TMP_TABLE'%{
'table_name': table_name,
'partition': partition_day,
'title_str': title_str
}
sparkSession.sql(sql_to_hive)
I don't think this is possible case to append data to the existing file.
But you can work around this case by using either of these ways
Approach1
Using Spark, write to intermediate temporary table and then insert overwrite to final table:
existing_df=spark.table("existing_hive_table") //get the current data from hive
current_df //new dataframe
union_df=existing_df.union(current_df)
union_df.write.mode("overwrite").saveAsTable("temp_table") //write the data to temp table
temp_df=spark.table("temp_table") //get data from temp table
temp_df.repartition(<number>).write.mode("overwrite").saveAsTable("existing_hive_table") //overwrite to final table
Approach2:
Hive(not spark) offers overwriting and select same table .i.e
insert overwrite table default.t1 partition(partiton_column)
select * from default.t1; //overwrite and select from same t1 table
If you are following this way then there needs to be hive job triggered once your spark job finishes.
Hive will acquire lock while running overwrite/select the same table so if any job which is writing to table will wait.
In Addition: Orc format will offer alter table concatenate which will merge small ORC files to create a new larger file.
alter table <db_name>.<orc_table_name> [partition_column="val"] concatenate;
We can also use distributeby,sortby clauses to control number of files, refer this and this link for more details.
Another Approach3 is by using hadoop fs -getMerge to merge all small files into one (this method works for text files and i haven't tried for orc,avro ..etc formats).
When you write the resulted dataframe:
result_df = sparkSession.sql(sql_to_hive)
set it’s mode to append:
result_df.write.mode(SaveMode.Append).
Related
I am working with Databricks Delta Live Tables, but have some problems with upserting some tables upstream. I know it is quite a long text below, but I tried to describe my problem as clear as possible. Let me know if some parts are not clear.
I have the following tables and flow:
Landing_zone -> This is a folder in which JSON files are added that contain data of inserted or updated records.
Raw_table -> This is the data in the JSON files but in table format. This table is in delta format. No transformations are done, except from transforming the JSON structure into a tabular structure (I did an explode and then creating columns from the JSON keys).
Intermediate_table -> This is the raw_table, but with some extra columns (depending on other column values).
To go from my landing zone to the raw table I have the following Pyspark code:
cloudfile = {"cloudFiles.format":"JSON",
"cloudFiles.schemaLocation": sourceschemalocation,
"cloudFiles.inferColumnTypes": True}
#dlt.view('landing_view')
def inc_view():
df = (spark
.readStream
.format('cloudFiles')
.options(**cloudFilesOptions)
.load(filpath_to_landing)
<Some transformations to go from JSON to tabular (explode, ...)>
return df
dlt.create_target_table('raw_table',
table_properties = {'delta.enableChangeDataFeed': 'true'})
dlt.apply_changes(target='raw_table',
source='landing_view',
keys=['id'],
sequence_by='updated_at')
This code works as expected. I run it, add a changes.JSON file to the landing zone, rerun the pipeline and the upserts are correctly applied to the 'raw_table'
(However, each time a new parquet file with all the data is created in the delta folder, I would expect that only a parquet file with the inserted and updated rows was added? And that some information about the current version was kept in the delta logs? Not sure if this is relevant for my problem. I already changed the table_properties of the 'raw_table' to enableChangeDataFeed = true. The readStream for 'intermediate_table' then has option(readChangeFeed, 'true')).
Then I have the following code to go from my 'raw_table' to my 'intermediate_table':
#dlt.table(name='V_raw_table', table_properties={delta.enableChangeDataFeed': 'True'})
def raw_table():
df = (spark.readStream
.format('delta')
.option('readChangeFeed', 'true')
.table('LIVE.raw_table'))
df = df.withColumn('ExtraCol', <Transformation>)
return df
ezeg
dlt.create_target_table('intermediate_table')
dlt.apply_changes(target='intermediate_table',
source='V_raw_table',
keys=['id'],
sequence_by='updated_at')
Unfortunately, when I run this, I get the error:
'Detected a data update (for example part-00000-7127bd29-6820-406c-a5a1-e76fc7126150-c000.snappy.parquet) in the source table at version 2. This is currently not supported. If you'd like to ignore updates, set the option 'ignoreChanges' to 'true'. If you would like the data update to be reflected, please restart this query with a fresh checkpoint directory.'
I checked in the 'ignoreChanges', but don't think this is what I want. I would expect that the autoloader would be able to detect the changes in the delta table and pass them through the flow.
I am aware that readStream only works with append, but that is why I would expect that after the 'raw_table' is updated, a new parquet file would be added to the delta folder with only the inserts and updates. This added parquet file is then detected by autoloader and could be used to apply the changes to the 'intermediate_table'.
Am I doing this the wrong way? Or am I overlooking something? Thanks in advance!
As readStream only works with appends, any change in the the source file will create issues downstream. The assumption that an update on "raw_table" will only insert a new parquet file is incorrect. Based on the settings like "optimized writes" or even without it, apply_changes can add or remove files. You can find this information in your "raw_table/_delta_log/xxx.json" under "numTargetFilesAdded" and "numTargetFilesRemoved".
Basically, "Databricks recommends you use Auto Loader to ingest only immutable files".
When you changed the settings to include the option '.option('readChangeFeed', 'true')', you should start with a full refresh(there is dropdown near start). Doing this will resolve the error 'Detected data update xxx', and your code should work for the incremental update.
I am trying to write spark dataframe into an existing delta table.
I do have multiple scenarios where I could save data into different tables as shown below.
SCENARIO-01:
I have an existing delta table and I have to write dataframe into that table with option mergeSchema since the schema may change for each load.
I am doing the same with below command by providing delta table path
finalDF01.write.format("delta").option("mergeSchema", "true").mode("append") \
.partitionBy("part01","part02").save(finalDF01DestFolderPath)
Just want to know whether this can be done by providing exisiting delta TABLE NAME instead of delta PATH.
This has been resolved by updating data write command as below.
finalDF01.write.format("delta").option("mergeSchema", "true").mode("append") \
.partitionBy("part01","part02").saveAsTable(finalDF01DestTableName)
Is this the correct way ?
SCENARIO 02:
I have to update the existing table if the record already exists and if not insert a new record.
For this I am currently doing as shown below.
spark.sql("SET spark.databricks.delta.schema.autoMerge.enabled = true")
DeltaTable.forPath(DestFolderPath)
.as("t")
.merge(
finalDataFrame.as("s"),
"t.id = s.id AND t.name= s.name")
.whenMatched().updateAll()
.whenNotMatched().insertAll()
.execute()
I tried with below script.
destMasterTable.as("t")
.merge(
vehMasterDf.as("s"),
"t.id = s.id")
.whenNotMatched().insertAll()
.execute()
but getting below error(even with alias instead of as).
error: value as is not a member of String
destMasterTable.as("t")
Here also I am using delta table path as destination, Is there any way so that we could provide delta TABLE NAME instead of TABLE PATH?
It will be good to provide TABLE NAME instead of TABLE PATH, In case if we chage the table path later will not affect the code.
I have not seen anywhere in databricks documentation providing table name along with mergeSchema and autoMerge.
Is it possible to do so?
To use existing data as a table instead of path you either were need to use saveAsTable from the beginning, or just register existing data in the Hive metastore using the SQL command CREATE TABLE USING, like this (syntax could be slightly different depending on if you're running on Databricks, or OSS Spark, and depending on the version of Spark):
CREATE TABLE IF NOT EXISTS my_table
USING delta
LOCATION 'path_to_existing_data'
after that, you can use saveAsTable.
For the second question - it looks like destMasterTable is just a String. To refer to existing table, you need to use function forName from the DeltaTable object (doc):
DeltaTable.forName(destMasterTable)
.as("t")
...
I would like to perform update and insert operation using spark
please find the image reference of existing table
Here i am updating id :101 location and inserttime and inserting 2 more records:
and writing to the target with mode overwrite
df.write.format("jdbc")
.option("url", "jdbc:mysql://localhost/test")
.option("driver","com.mysql.jdbc.Driver")
.option("dbtable","temptgtUpdate")
.option("user", "root")
.option("password", "root")
.option("truncate","true")
.mode("overwrite")
.save()
After executing the above command my data is corrupted which is inserted into db table
Data in the dataframe
Could you please let me know your observations and solutions
Spark JDBC writer supports following modes:
append: Append contents of this :class:DataFrame to existing data.
overwrite: Overwrite existing data.
ignore: Silently ignore this operation if data already exists.
error (default case): Throw an exception if data already exists
https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html
Since you are using "overwrite" mode it recreate your table as per then column length, if you want your own table definition create table first and use "append" mode
i would like to perform update and insert operation using spark
There is no equivalent in to SQL UPDATE statement with Spark SQL. Nor is there an equivalent of the SQL DELETE WHERE statement with Spark SQL. Instead, you will have to delete the rows requiring update outside of Spark, then write the Spark dataframe containing the new and updated records to the table using append mode (in order to preserve the remaining existing rows in the table).
In case where you need to perform UPSERT / DELETE operations in your pyspark code, i suggest you to use pymysql libary, and execute your upsert/delete operations. Please check this post for more info, and code sample for reference : Error while using INSERT INTO table ON DUPLICATE KEY, using a for loop array
Please modify the code sample as per your needs.
I wouldn't recommend TRUNCATE, since it would actually drop the table, and create new table. While doing this, the table may lose column level attributes that were set earlier...so be careful while using TRUNCATE, and be sure, if it's ok for dropping the table/recreate the table.
Upsert logic is working fine when following below steps
df = (spark.read.format("csv").
load("file:///C:/Users/test/Desktop/temp1/temp1.csv", header=True,
delimiter=','))
and doing this
(df.write.format("jdbc").
option("url", "jdbc:mysql://localhost/test").
option("driver", "com.mysql.jdbc.Driver").
option("dbtable", "temptgtUpdate").
option("user", "root").
option("password", "root").
option("truncate", "true").
mode("overwrite").save())
Still, I am unable to understand the logic why its failing when i am writing using the data frame directly
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 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')""")