I want access hive data using spark:
%spark
val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc)
sqlContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
sqlContext.sql(LOAD DATA LOCAL INPATH '//filepath' INTO TABLE src)
sqlContext.sql("FROM src SELECT key, value").collect().foreach(println)
However I am getting error:
:4: error: ')' expected but '(' found.
sqlContext.sql("FROM src SELECT key, value").collect().foreach(println)
How to resolve this error?
You should use standard SQL syntax:
sqlContext.sql("SELECT key, value FROM src").show()
What's more, every sql command should have String as an argument, second command is without ""
sqlContext.sql("LOAD DATA LOCAL INPATH '//filepath' INTO TABLE src")
can you try this?
import org.apache.spark.sql.hive.HiveContext
val sqlContext = new HiveContext(sc) # creating hive context
sqlContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
sqlContext.sql("LOAD DATA LOCAL INPATH '//filepath' INTO TABLE src")
val srcRDD = sqlContext.sql("SELECT key, value FROM src")
srcRDD.collect().foreach(println) # printing the data
Related
I have a Cassandra table that is created as the following(in cqlsh)
CREATE TABLE blog.session( id int PRIMARY KEY, visited text);
I write data to Cassandra and it looks like this
id | visited
1 | Url1-Url2-Url3
I then try to read it using spark Cassandra connector(2.5.1).
val sparkSession = SparkSession.builder()
.master("local")
.appName("ReadFromCass")
.config("spark.cassandra.connection.host", "localhost")
.config("spark.cassandra.connection.port", "9042")
.getOrCreate()
import sparkSession.implicits._
val readSessions = sparkSession.sqlContext
.read
.cassandraFormat("table1", "keyspace1").load().show()
However, it seems to be unable to read the visited since it is a text object with dashes in between words. The error occurs as
org.apache.spark.unsafe.types.UTF8String is not a valid external type for schema of string
any ideas on why spark is unable to read this and how to fix it?
The error seemed to be the version of the spark-cassandra-connector. Instead of using "2.5.1" use "3.0.0-beta"
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'm a table in cassandra tfm.foehis that have data.
When i did the first charge of data from spark to cassandra, I used this set of commands:
import org.apache.spark.sql.functions._
import com.datastax.spark.connector._
import org.apache.spark.sql.cassandra._
val wkdir="/home/adminbigdata/tablas/"
val fileIn= "originales/22_FOEHIS2.csv"
val fileOut= "22_FOEHIS_PRE2"
val fileCQL= "22_FOEHISCQL"
val data = sc.textFile(wkdir + fileIn).filter(!_.contains("----")).map(_.trim.replaceAll(" +", "")).map(_.dropRight(1)).map(_.drop(1)).map(_.replaceAll(",", "")).filter(array => array(6) != "MOBIDI").filter(array => array(17) != "").saveAsTextFile(wkdir + fileOut)
val firstDF = spark.read.format("csv").option("header", "true").option("inferSchema", "true").option("mode", "DROPMALFORMED").option("delimiter", "|").load(wkdir + fileOut)
val columns: Array[String] = firstDF.columns
val reorderedColumnNames: Array[String] = Array("hoclic","hodtac","hohrac","hotpac","honrac","hocdan","hocdrs","hocdsl","hocol","hocpny","hodesf","hodtcl","hodtcm","hodtea","hodtra","hodtrc","hodtto","hodtua","hohrcl","hohrcm","hohrea","hohrra","hohrrc","hohrua","holinh","holinr","honumr","hoobs","hooe","hotdsc","hotour","housca","houscl","houscm","housea","houser","housra","housrc")
val secondDF= firstDF.select(reorderedColumnNames.head, reorderedColumnNames.tail: _*)
secondDF.write.cassandraFormat("foehis", "tfm").save()
But when I load new data using the same script, I get errors. I don't know what's wrong?
This is the message:
java.lang.UnsupportedOperationException: 'SaveMode is set to ErrorIfExists and Table
tfm.foehis already exists and contains data.
Perhaps you meant to set the DataFrame write mode to Append?
Example: df.write.format.options.mode(SaveMode.Append).save()" '
The error message clearly says you that you need to use Append mode & shows what you can do with it. In your case it happens because destination table already exists, and writing mode is set to "error if exists". If you still want to write data, the code should be following:
import org.apache.spark.sql.SaveMode
secondDF.write.cassandraFormat("foehis", "tfm").mode(SaveMode.Append).save()
I have a sample application working to read from csv files into a dataframe. The dataframe can be stored to a Hive table in parquet format using the method
df.saveAsTable(tablename,mode).
The above code works fine, but I have so much data for each day that i want to dynamic partition the hive table based on the creationdate(column in the table).
is there any way to dynamic partition the dataframe and store it to hive warehouse. Want to refrain from Hard-coding the insert statement using hivesqlcontext.sql(insert into table partittioin by(date)....).
Question can be considered as an extension to :How to save DataFrame directly to Hive?
any help is much appreciated.
I believe it works something like this:
df is a dataframe with year, month and other columns
df.write.partitionBy('year', 'month').saveAsTable(...)
or
df.write.partitionBy('year', 'month').insertInto(...)
I was able to write to partitioned hive table using df.write().mode(SaveMode.Append).partitionBy("colname").saveAsTable("Table")
I had to enable the following properties to make it work.
hiveContext.setConf("hive.exec.dynamic.partition", "true")
hiveContext.setConf("hive.exec.dynamic.partition.mode", "nonstrict")
I also faced same thing but using following tricks I resolved.
When we Do any table as partitioned then partitioned column become case sensitive.
Partitioned column should be present in DataFrame with same name (case sensitive). Code:
var dbName="your database name"
var finaltable="your table name"
// First check if table is available or not..
if (sparkSession.sql("show tables in " + dbName).filter("tableName='" +finaltable + "'").collect().length == 0) {
//If table is not available then it will create for you..
println("Table Not Present \n Creating table " + finaltable)
sparkSession.sql("use Database_Name")
sparkSession.sql("SET hive.exec.dynamic.partition = true")
sparkSession.sql("SET hive.exec.dynamic.partition.mode = nonstrict ")
sparkSession.sql("SET hive.exec.max.dynamic.partitions.pernode = 400")
sparkSession.sql("create table " + dbName +"." + finaltable + "(EMP_ID string,EMP_Name string,EMP_Address string,EMP_Salary bigint) PARTITIONED BY (EMP_DEP STRING)")
//Table is created now insert the DataFrame in append Mode
df.write.mode(SaveMode.Append).insertInto(empDB + "." + finaltable)
}
it can be configured on SparkSession in that way:
spark = SparkSession \
.builder \
...
.config("spark.hadoop.hive.exec.dynamic.partition", "true") \
.config("spark.hadoop.hive.exec.dynamic.partition.mode", "nonstrict") \
.enableHiveSupport() \
.getOrCreate()
or you can add them to .properties file
the spark.hadoop prefix is needed by Spark config (at least in 2.4) and here is how Spark sets this config:
/**
* Appends spark.hadoop.* configurations from a [[SparkConf]] to a Hadoop
* configuration without the spark.hadoop. prefix.
*/
def appendSparkHadoopConfigs(conf: SparkConf, hadoopConf: Configuration): Unit = {
SparkHadoopUtil.appendSparkHadoopConfigs(conf, hadoopConf)
}
This is what works for me. I set these settings and then put the data in partitioned tables.
from pyspark.sql import HiveContext
sqlContext = HiveContext(sc)
sqlContext.setConf("hive.exec.dynamic.partition", "true")
sqlContext.setConf("hive.exec.dynamic.partition.mode",
"nonstrict")
This worked for me using python and spark 2.1.0.
Not sure if it's the best way to do this but it works...
# WRITE DATA INTO A HIVE TABLE
import pyspark
from pyspark.sql import SparkSession
spark = SparkSession \
.builder \
.master("local[*]") \
.config("hive.exec.dynamic.partition", "true") \
.config("hive.exec.dynamic.partition.mode", "nonstrict") \
.enableHiveSupport() \
.getOrCreate()
### CREATE HIVE TABLE (with one row)
spark.sql("""
CREATE TABLE IF NOT EXISTS hive_df (col1 INT, col2 STRING, partition_bin INT)
USING HIVE OPTIONS(fileFormat 'PARQUET')
PARTITIONED BY (partition_bin)
LOCATION 'hive_df'
""")
spark.sql("""
INSERT INTO hive_df PARTITION (partition_bin = 0)
VALUES (0, 'init_record')
""")
###
### CREATE NON HIVE TABLE (with one row)
spark.sql("""
CREATE TABLE IF NOT EXISTS non_hive_df (col1 INT, col2 STRING, partition_bin INT)
USING PARQUET
PARTITIONED BY (partition_bin)
LOCATION 'non_hive_df'
""")
spark.sql("""
INSERT INTO non_hive_df PARTITION (partition_bin = 0)
VALUES (0, 'init_record')
""")
###
### ATTEMPT DYNAMIC OVERWRITE WITH EACH TABLE
spark.sql("""
INSERT OVERWRITE TABLE hive_df PARTITION (partition_bin)
VALUES (0, 'new_record', 1)
""")
spark.sql("""
INSERT OVERWRITE TABLE non_hive_df PARTITION (partition_bin)
VALUES (0, 'new_record', 1)
""")
spark.sql("SELECT * FROM hive_df").show() # 2 row dynamic overwrite
spark.sql("SELECT * FROM non_hive_df").show() # 1 row full table overwrite
df1.write
.mode("append")
.format('ORC')
.partitionBy("date")
.option('path', '/hdfs_path')
.saveAsTable("DB.Partition_tablename")
It will create the partition with "date" column values and will also write as Hive External Table in hive from spark DF.