spark.sql("SHOW TABLE EXTENDED IN DB LIKE 'TABLE'")
Beeline >>SHOW TABLE EXTENDED IN DB LIKE 'TABLE';
Both queries have different results.
If I run the same query in Spark it is giving different result than Hive. Format and lastUpdatedTime is missing in Spark SQL.
If anyone have idea then please let me know how to see lastUpdatedTime of Hive table from Spark SQL
Try this -
scala> val df = spark.sql(s"describe extended ${db}.${table_name}").select("data_type").where("col_name == 'Table Properties'")
df: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [data_type: string]
scala> df.map(r => r.getString(0).split(",")(1).trim).collect
res39: Array[String] = Array(last_modified_time=1539848078)
scala> df.map(r => r.getString(0).split(",")(1).trim.split("=")(1)).collect.mkString
res41: String = 1539848078
Related
I have this two line in spark, I want to get the equivalent in SparkSQL (im working with python env)
df = spark_df.filter(col["col_name".lower()].rlike("[0-9]{9}$")).count()
spark_df = spark_df.withColumn(columnname, F.to_date(F.col((columnname, ),"yyyyMMdd"))
For spark sql first convert dataframe to temp view then run sql.
Example:
spark_df.createOrReplaceTempView("tmp")
df=spark.sql("""select count(*) from tmp where lower(col_name) rlike("[0-9]{9}$") """).collect()[0][0]
spark_df = spark.sql("""select *, to_date(columnname,"yyyyMMdd") columnname from tmp """)
I have a dataframe with multiple columns out of which one column is map(string,string) type. I'm able to print this dataframe having column as map which gives data as Map("PUN" -> "Pune"). I want to write this dataframe to hive table (stored as avro) which has same column with type map.
Df.withcolumn("cname", lit("Pune"))
withcolumn("city_code_name", map(lit("PUN"), col("cname"))
Df.show(false)
//table - created external hive table..stored as avro..with avro schema
After removing this map type column I'm able to save the dataframe to hive avro table.
Save way to hive table:
spark.save - saving avro file
spark.sql - creating partition on hive table with avro file location
see this test case as an example from spark tests
test("Insert MapType.valueContainsNull == false") {
val schema = StructType(Seq(
StructField("m", MapType(StringType, StringType, valueContainsNull = false))))
val rowRDD = spark.sparkContext.parallelize(
(1 to 100).map(i => Row(Map(s"key$i" -> s"value$i"))))
val df = spark.createDataFrame(rowRDD, schema)
df.createOrReplaceTempView("tableWithMapValue")
sql("CREATE TABLE hiveTableWithMapValue(m Map <STRING, STRING>)")
sql("INSERT OVERWRITE TABLE hiveTableWithMapValue SELECT m FROM tableWithMapValue")
checkAnswer(
sql("SELECT * FROM hiveTableWithMapValue"),
rowRDD.collect().toSeq)
sql("DROP TABLE hiveTableWithMapValue")
}
also if you want save option then you can try with saveAsTable as showed here
Seq(9 -> "x").toDF("i", "j")
.write.format("hive").mode(SaveMode.Overwrite).option("fileFormat", "avro").saveAsTable("t")
yourdataframewithmapcolumn.write.partitionBy is the way to create partitions.
You can achieve that with saveAsTable
Example:
Df\
.write\
.saveAsTable(name='tableName',
format='com.databricks.spark.avro',
mode='append',
path='avroFileLocation')
Change the mode option to whatever suits you
I am trying to create a datapipeline where the incomng data is stored into parquet and i create and external hive table and users can query the hive table and retrieve data .I am able to save the parquet data and retrieve it directly but when i query the hive table its not returning any rows. I did the following test setup
--CREATE EXTERNAL HIVE TABLE
create external table emp (
id double,
hire_dt timestamp,
user string
)
stored as parquet
location '/test/emp';
Now created dataframe on some data and saved to parquet .
---Create dataframe and insert DATA
val employeeDf = Seq(("1", "2018-01-01","John"),("2","2018-12-01", "Adam")).toDF("id","hire_dt","user")
val schema = List(("id", "double"), ("hire_dt", "date"), ("user", "string"))
val newCols= schema.map ( x => col(x._1).cast(x._2))
val newDf = employeeDf.select(newCols:_*)
newDf.write.mode("append").parquet("/test/emp")
newDf.show
--read the contents directly from parquet
val sqlcontext=new org.apache.spark.sql.SQLContext(sc)
sqlcontext.read.parquet("/test/emp").show
+---+----------+----+
| id| hire_dt|user|
+---+----------+----+
|1.0|2018-01-01|John|
|2.0|2018-12-01|Adam|
+---+----------+----+
--read from the external hive table
spark.sql("select id,hire_dt,user from emp").show(false)
+---+-------+----+
|id |hire_dt|user|
+---+-------+----+
+---+-------+----+
As shown above i am able to see the data if i read from parquet directly but not from hive .The question is what i am doing wrong here ? What i am i doing wrong that the hive isnt getting the data. I thought msck repair may be a reason but i get error if i try to do msck repair table saying table not partitioned.
Based on your create table statement, you have used location as /test/emp but while writing data, you are writing at /tenants/gwm/idr/emp. So you will not have data at /test/emp.
CREATE EXTERNAL HIVE TABLE create external table emp ( id double, hire_dt timestamp, user string ) stored as parquet location '/test/emp';
Please re-create external table as
CREATE EXTERNAL HIVE TABLE create external table emp ( id double, hire_dt timestamp, user string ) stored as parquet location '/tenants/gwm/idr/emp';
Apart from the answer given by Ramdev below, you also need to be cautious of using the correct datatype around date/timestamp; as 'date' type is not supported by parquet when creating a hive table.
For that you can change the 'date' type for column 'hire_dt' to 'timestamp'.
Otherwise there will be a mismatch in data you persisting through spark and trying to read in hive (or hive SQL). Keeping it to 'timestamp' at both places will resolve the issue. I hope it helps.
Do you have enableHiveSupport() in your sparkSession builder() statement. Are you able to connect to hive metastore? Try doing show tables/databases in your code to see if you can display tables present at your hive location?
i got this working with below chgn.
val dfTransformed = employeeDf.withColumn("id", employeeDf.col("id").cast(DoubleType))
.withColumn("hire_dt", employeeDf.col("hire_dt".cast(TimestampType))
So basically the issue was datatype mismatch and some how the original code the cast doesn't seem to work. So i did an explicit cast and then write it goes fine and able to query back as well.Logically both are doing the same not sure why the original code not working.
val employeeDf = Seq(("1", "2018-01-01","John"),("2","2018-12-01", "Adam")).toDF("id","hire_dt","user")
val dfTransformed = employeeDf.withColumn("id", employeeDf.col("id").cast(DoubleType))
.withColumn("hire_dt", employeeDf.col("hire_dt".cast(TimestampType))
dfTransformed.write.mode("append").parquet("/test/emp")
dfTransformed.show
--read the contents directly from parquet
val sqlcontext=new org.apache.spark.sql.SQLContext(sc)
sqlcontext.read.parquet("/test/emp").show
+---+----------+----+
| id| hire_dt|user|
+---+----------+----+
|1.0|2018-01-01|John|
|2.0|2018-12-01|Adam|
+---+----------+----+
--read from the external hive table
spark.sql("select id,hire_dt,user from emp").show(false)
+---+----------+----+
| id| hire_dt|user|
+---+----------+----+
|1.0|2018-01-01|John|
|2.0|2018-12-01|Adam|
+---+----------+----+
I am running the following spark sql:
test("SparkSQLTest") {
val spark = SparkSession.builder().master(s"local[1]").config("spark.sql.cbo.enabled", true).appName("SparkSQLTest").getOrCreate()
spark.createDataFrame(Seq(Person("Tom", 31), Person("Jack", 22))).createOrReplaceTempView("t")
val df = spark.sql("select name, '' XYZ, '' 水果, age from t ")
df.show(false)
}
I add a constant, name it to 水果， but Spark SQL doesn't recognize it
While creating the spark session object set the encoding option as SJIS. It’s a character encoding
I read data from Oracle via spark JDBC connection to a DataFrame. I have a column which is obviously StringType in dataframe.
Now I want to persist this in Hive, but as datatype Varchar(5). I know the string would be truncated but it is ok.
I tried using UDFs which didn't work since dataframe does not have varchar or char types. I also created a temporary view in Hive using:
val tv = df.createOrReplaceTempView("t_name")
val df = spark.sql("select cast(col_name as varchar(5)) from tv")
But then when i printSchema, i still see a string type.
How can I make I save it as a varchar column in Hive table ?
Try creating Hive table("dbName.tableName") with required schema (varchar(5) in this case) and insert into the table directly from Dataframe like below.
df.write.insertInto("dbName.tableName" ,overwrite = False)