I have used SQL in Spark, in this example:
results = spark.sql("select * from ventas")
where ventas is a dataframe, previosuly cataloged like a table:
df.createOrReplaceTempView('ventas')
but I have seen other ways of working with SQL in Spark, using the class SqlContext:
df = sqlContext.sql("SELECT * FROM table")
What is the difference between both of them?
Thanks in advance
From a user's perspective (not a contributor), I can only rehash what the developer's provided in the upgrade notes:
Upgrading From Spark SQL 1.6 to 2.0
SparkSession is now the new entry point of Spark that replaces the old SQLContext and HiveContext. Note that the old SQLContext and HiveContext are kept for backward compatibility. A new catalog interface is accessible from SparkSession - existing API on databases and tables access such as listTables, createExternalTable, dropTempView, cacheTable are moved here.
Before 2.0, the SqlContext needed an extra call to the factory that creates it. With SparkSession, they made things a lot more convenient.
If you take a look at the source code, you'll notice that the SqlContext class is mostly marked #deprecated. Closer inspection shows that the most commonly used methods simply call sparkSession.
For more info, take a look at the developer notes, Jira issues, conference talks on spark 2.0, and Databricks blog.
Before Spark 2.x SQLContext was build with help of SparkContext but after Spark 2.x SparkSession was introduced which have the functionality of HiveContext and SQLContect both.So no need of creating SQLContext separatly.
**before Spark2.x**
sCont = SparkContext()
sqlCont = SQLContext(sCont)
**after Spark 2.x:**
spark = SparkSession()
Sparksession is the preferred way of working with Spark object now. Both Hivecontext and SQLContext are available as a part of this single object SparkSession.
You are using the latest syntax by creating a view df.createOrReplaceTempView('ventas').
Next create the df1 as javaobject
df1=sqlcontext.sql("select col1,col2,col3 from table")
Next create df2 as DATAFRAME
df2=spark.sql("select col1,col2,col3 from table")
Check the difference using type(df2) and type(df1)
Related
when i am storing hive table in one dataframe using HiveContext and DB2 table in another dataframe using sqlcontext on querying both the dataframes it is not detecting the Db2 while it detects hive. What is a common sqlcontext that can be used?
TL;DR Use the same context for all tables.
If you need Hive support use HiveContext or SparkSession with Hive support. Don't create a separate session to connect to specific DataSource.
Can anyone explain the difference between SparkContext, SQLContext, HiveContext and SparkSession EntryPoints and each one's usecases.
SparkContext is used for basic RDD API on both Spark1.x and Spark2.x
SparkSession is used for DataFrame API and Struct Streaming API on Spark2.x
SQLContext & HiveContext are used for DataFrame API on Spark1.x and deprecated from Spark2.x
Spark Context is Class in Spark API which is the first stage to build the spark application. Functionality of the spark context is to create memory in RAM we call this as driver memory, allocation of number of executers and cores in short its all about the cluster management. Spark Context can be used to create RDD and shared variables. SparkContext is a Class to access this we need to create object of it.
This way we can create Spark Context :: var sc=new SparkContext()
Spark Session this is new Object added since spark 2.x which is replacement of Sql Context and Hive Context.
Earlier we had two options like one is Sql Context which is way to do sql operation on Dataframe and second is Hive Context which manage the Hive connectivity related stuff and fetch/insert the data from/to the hive tables.
Since 2.x came We can create SparkSession for the SQL operation on Dataframe and if you have any Hive related work just call the Method enablehivesupport() then you can use the SparkSession for the both Dataframe and hive related SQL operations.
This way we can create SparkSession for Sql operation on Dataframe
val sparksession=SparkSession.builder().getOrCreate();
Second way is to create SparkSession for Sql operation on Dataframe as well as Hive Operation.
val sparkSession=SparkSession.builder().enableHiveSupport().getOrCreate()
I am new to the Hadoop ecosystem and I am still confused with few things. I am using Spark 1.6.0 (Hive 1.1.0-cdh5.8.0, Hadoop 2.6.0-cdh5.8.0)
I have some Hive table that exist and I can do some SQL queries using HUE web interface with Hive (map reduce) and Impala (mpp).
I am now using pySpark (I think behind this is pyspark-shell) and I wanted to understand and test HiveContext and SQLContext. There are many thready that discussed the differences between the two and for various version of Spark.
With Hive context, I have no issue to query the Hive tables:
from pyspark.sql import HiveContext
mysqlContext = HiveContext(sc)
FromHive = mysqlContext.sql("select * from table.mytable")
FromHive.count()
320
So far so good. Since SQLContext is subset of HiveContext, I was thinking that a basic SQL select should work:
from pyspark.sql import SQLContext
sqlSparkContext = SQLContext(sc)
FromSQL = mysqlContext.sql("select * from table.mytable")
FromSQL.count()
Py4JJavaError: An error occurred while calling o81.sql.
: org.apache.spark.sql.AnalysisException: Table not found: `table`.`mytable`;
I added the hive-site.xml to pyspark-shell. When running
sc._conf.getAll(
I see:
('spark.yarn.dist.files', '/etc/hive/conf/hive-site.xml'),
My questions are:
Can I acess Hive table with SQLContext for simple queries (I know
HiveContext is more powerfull but for me this is just to understand
things)
If this is possible what is missing ? I couldn't find any info apart
from the hive-site.xml that I tried but doesn't seems to work
Thanks a lot
Cheers
Fabien
As mentioned in other answer, you can't use SQLContext to access Hive tables, they've given a seperate HiveContext in Spark 1.x.x which is basically an extension of SQLContext.
Reason::
Hive uses an external metastore to keep all the metadata, for example the information about db and tables. This metastore can be configured to be kept in MySQL etc. Default is derby.
This done so that all the users accessing Hive may see all the contents facilitated by metastore.
Derby creates a private metastore as a directory metastore_db in the directory from where the spark app is executed. Since this metastore is private, what ever you create or edit in this session, will not be accessible to anyone else. SQLContext basically facilitates a connection to a private metastore.
Needless to say, in Spark 2.x.x they've merged the two into SparkSession which acts as a singular entry point to spark. You can enable Hive support while creating SparkSession by .enableHiveSupport()
You cannot use standard SQLContext to access Hive directly. To work with Hive you need Spark binaries built with Hive support and HiveContext.
You could use use JDBC data source, but it won't be acceptable performance wise for large scale processing.
To access SQLContext tables, you need to register it temporarily. Then you can easily make SQL queries on it. Suppose you have some data in the form of JSON. You can make it in dataframe.
Like below:
from pyspark.sql import SQLContext
sqlSparkContext = SQLContext(sc)
df = sqlSparkContext.read.json("your json data")
sql_df = df.registerTempTable("mytable")
FromSQL = sqlSparkContext.sql("select * from mytable")
FromSQL.show()
Also you can collect the SQL data in row type array as below:-
r = FromSSQL.collect()
print r.column_Name
Try without keeping sc into sqlContext,I think when we create sqlContext object with sc spark is trying to call HiveContext but we are having sqlContext instead
>>>df=sqlContext.sql("select * from <db-name>.<table-name>")
Use the superset of SQL Context i.e HiveContext to Connect and load the hive tables to spark dataframes
>>>df=HiveContext(sc).sql("select * from <db-name>.<table-name>")
(or)
>>>df=HiveContext(sc).table("default.text_Table")
(or)
>>> hc=HiveContext(sc)
>>> df=hc.sql("select * from default.text_Table")
In Spark 1.6, a SparkEnv is automatically created after the creating a new SparkContext object.
In Spark 2.0, SparkSession was introduced as the entry point to Spark SQL.
Is SparkEnv created automatically after the creation of SparkSession in Spark 2?
Yes, SparkEnv, SparkConf and SparkContext are all automatically created when SparkSession is created (and that's why corresponding code in Spark SQL is more high-level and hopefully less error-prone).
SparkEnv is a part of Spark runtime infrastructure and is required to have all the Spark Core's low-level services up and running before you can use the high-level APIs in Spark SQL (or Spark MLlib). Nothing has changed here.
scala> :type spark
org.apache.spark.sql.SparkSession
scala> spark.sparkContext
res1: org.apache.spark.SparkContext = org.apache.spark.SparkContext#1e86506c
We see that,
Spark context available as 'sc'.
Spark session available as 'spark'.
I read spark session includes spark context, streaming context, hive context ... If so, then why are we not able to create an rdd by using a spark session instead of a spark context.
scala> val a = sc.textFile("Sample.txt")
17/02/17 16:16:14 WARN util.SizeEstimator: Failed to check whether UseCompressedOops is set; assuming yes
a: org.apache.spark.rdd.RDD[String] = Sample.txt MapPartitionsRDD[1] at textFile at <console>:24
scala> val a = spark.textFile("Sample.txt")
<console>:23: error: value textFile is not a member of org.apache.spark.sql.SparkSession
val a = spark.textFile("Sample.txt")
As shown above, sc.textFile succeeds in creating an RDD but not spark.textFile.
In Spark 2+, Spark Context is available via Spark Session, so all you need to do is:
spark.sparkContext().textFile(yourFileOrURL)
see the documentation on this access method here.
Note that in PySpark this would become:
spark.sparkContext.textFile(yourFileOrURL)
see the documentation here.
In earlier versions of spark, spark context was entry point for Spark. As RDD was main API, it was created and manipulated using context API’s.
For every other API,we needed to use different contexts.For streaming, we needed StreamingContext, for SQL sqlContext and for hive HiveContext.
But as DataSet and Dataframe API’s are becoming new standard API’s Spark need an entry point build for them. So in Spark 2.0, Spark have a new entry point for DataSet and Dataframe API’s called as Spark Session.
SparkSession is essentially combination of SQLContext, HiveContext and future StreamingContext.
All the API’s available on those contexts are available on spark session also. Spark session internally has a spark context for actual computation.
sparkContext still contains the method which it had in previous
version .
methods of sparkSession can be found here
It can be created in the following way-
val a = spark.read.text("wc.txt")
This will create a dataframe,If you want to convert it to RDD then use-
a.rdd Please refer the link below,on dataset API-
http://cdn2.hubspot.net/hubfs/438089/notebooks/spark2.0/Dataset.html