I have a hive metastore setup on an Azure SQL Database that is used by HDInsight clusters. When I go into the cluster and use the schematool to verify versions, it returns Hive Distribution 1.2.1 and Metastore schema version 2.1.2.
This becomes troublesome when I try to connect to this metastore using databricks, which returns errors saying that 2.1.2 is not a valid metastore schema version. I try substituting the version with 2.1.1 or 2.1.0 and it errors out as well. I've tried looking at official distributions from apache (https://hive.apache.org/downloads.html) and don't see it listed.
Has anyone else come across this error? Am I missing something or is this a bug in HDInsight's setting up of the metastore?
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I'm new to spark. Now I want to use spark to read some data and write it to the tables defined by hive. I'm using spark3.3.1 and hadoop 3.3.2, and now, can I download hive3 and config spark3 work together? Because some materials I found from internet told me spark can't work with all versions of hive
thanks
From Spark 3.2.1 documentation it is compatible with Hive 3.1.0 if the versions of spark and hive can be modified I would suggest you to use the above mentioned combination to start with.
I try to integrate hive 3.1.2 with spark 3.2.1. There is a hive fork for spark 3:
https://github.com/forsre/hive3.1.2
You can use it to recompile hive with spark 3 and hive on spark can work.
But spark thrift server is incompatible with hive 3. Apache kyuubi is suggested to replace spark thrift server and hiveserver2.
https://kyuubi.apache.org/
You can just use standard hive 3.1.2 and spart 3.2.1 package with kyuubi 1.6.0 to make them work.
I am using Spark 3.0.1 on HDP 3.1.4. Everything is running well except Spark SQL can't honor Ranger standard SQL policy.
In the past days, I tried the solution which found from the community, the hive warehouse connector and spark-authorizer and spark-llap.
Unfortunately I can't solve it. Seems the code was not maintained and the latest release version doesn't support Spark 3.0. I saw many people are also struggling in this problem.
Is there any suggestion to make Spark SQL adopt Ranger column/ row level permission policy ? Any idea are appreciated. Thank you.
hive warehouse connector, it works on spark 2.3.1, but not 3.0.
spark-authorizer, spark-llap both are version not compatible error.
The version is Spark 3.0.1, HDP 3.1.1, Hive 3.1.0, Ranger 1.2.0
I want to read data from cassandra node in my client node on :
This is what i tried :
spark-shell --jars /my-dir/spark-cassandra-connector_2.11-2.3.2.jar.
val df = spark.read.format("org.apache.spark.sql.cassandra")\
.option("keyspace","my_keyspace")\
.option("table","my_table")\
.option("spark.cassandra.connection.host","Hostname of my Cassandra node")\
.option("spark.cassandra.connection.port","9042")\
.option("spark.cassandra.auth.password","mypassword)\
.option("spark.cassandra.auth.username","myusername")\
.load
I'm getting this error: java.lang.NoClassDefFoundError: Could not initialize class org.apache.spark.sql.cassandra.DefaultSource$
and
java.lang.NoClassDefFoundError: org/apache/commons/configuration/ConfigurationException.
Am I missing any properties? What this error is for ? How would I resolve this ?
Spark-version:2.3.2, DSE version 6.7.8
The Spark Cassandra Connector itself depends on the number of other dependencies, that could be missing here - this happens because you're providing only one jar, and not all required dependencies.
Basically, in your case you need to have following choice:
If you're running this on the DSE node, then you can use built-in Spark, if the cluster has Analytics enabled - in this case, all jars and properties are already provided, and you only need to provide username and password when starting spark shell via dse -u user -p password spark
if you're using external Spark, then it's better to use so-called BYOS (bring your own spark) - special version of the Spark Cassandra Connector with all dependencies bundled inside, and you can download jar from DataStax's Maven repo, and use with --jars
you can still use open source Spark Cassandra Connector, but in this case, it's better to use --packages com.datastax.spark:spark-cassandra-connector_2.11:2.3.2 so Spark will able to fetch all dependencies automatically.
P.S. In case of open source Spark Cassandra Connector I would recommend to use version 2.5.1 or higher, although it requires Spark 2.4.x (although 2.3.x may work) - this version has improved support for DSE, plus a lot of the new functionality not available in the earlier versions. Plus for that version there is a version that includes all required dependencies (so-called assembly) that you can use with --jars if your machine doesn't have access to the internet.
I have installed spark 2.4.0 on a clean ubuntu instance. Spark dataframes work fine but when I try to use spark.sql against a dataframe such as in the example below,i am getting an error "Failed to access metastore. This class should not accessed in runtime."
spark.read.json("/data/flight-data/json/2015-summary.json")
.createOrReplaceTempView("some_sql_view")
spark.sql("""SELECT DEST_COUNTRY_NAME, sum(count)
FROM some_sql_view GROUP BY DEST_COUNTRY_NAME
""").where("DEST_COUNTRY_NAME like 'S%'").where("sum(count) > 10").count()
Most of the fixes that I have see in relation to this error refer to environments where hive is installed. Is hive required if I want to use sql statements against dataframes in spark or am i missing something else?
To follow up with my fix. The problem in my case was that Java 11 was the default on my system. As soon as I set Java 8 as the default metastore_db started working.
Yes, we can run spark sql queries on spark without installing hive, by default hive uses mapred as an execution engine, we can configure hive to use spark or tez as an execution engine to execute our queries much faster. Hive on spark hive uses hive metastore to run hive queries. At the same time, sql queries can be executed through spark. If spark is used to execute simple sql queries or not connected with hive metastore server, its uses embedded derby database and a new folder with name metastore_db will be created under the user home folder who executes the query.
I read the documentation of spark and hbase :
http://hbase.apache.org/book.html#spark
I can see that the last stable version of HBase is 1.1.2, but I also see that apidocs is on version 2.0.0-SNAPSHOT and that the apidoc of spark is empty.
I am confused, why the apidocs and HBase version don't match?
My goal is to use Spark and HBase (bulkGet, bulkPut..etc). How do I know in which HBase version those functions have been implemented?
If someone have complementary documentation on this, it will be awesome.
I am on hbase-0.98.13-hadoop1.
Below is the main JIRA ticket for Spark integration into HBase, the target version is 2.0.0 which still under development, need waiting for the release, or build a version from source code by your own
https://issues.apache.org/jira/browse/HBASE-13992
Within the ticket, there are several links for documentation.
If you just want to access HBase from Spark RDD, you can consider it as normal Hadoop datasource, based on HBase specific TableInputFormat and TableOutputFormat
As of now, Spark doesn't come with HBase API as it has for the hive, you have manually put HBase jars in spark's classpath in spark-default.conf file.
see below link it has complete information about how to connect to HBase:
http://www.abcn.net/2014/07/lighting-spark-with-hbase-full-edition.html