I just installed presto and when I use the presto-cli to query hive data, I get the following error:
$ ./presto --server node6:8080 --catalog hive --schema default
presto:default> show tables;
Query 20131113_150006_00002_u8uyp failed: Table hive.information_schema.tables does not exist
The config.properties is:
coordinator=true
datasources=jmx,hive
http-server.http.port=8080
presto-metastore.db.type=h2
presto-metastore.db.filename=/root/h2
task.max-memory=1GB
discovery-server.enabled=true
discovery.uri=`http://node6:8080`
And the hive.properties is:
connector.name=hive-cdh4
hive.metastore.uri=thrift://node6:9083
The hadoop distribution I used is CDH 4.4. I believe it's properly installed and hive can process queries successfully on its own.
Can anyone help me work it out? Any ideas will be appreciated.
As recommended by the Getting Started, I created a controller (jmx only) and a separate worker (jmx,hive), each on separate machines.
What finally solved this for me was to specify the worker's hostname and http-server.http.port as the --server argument to presto. When specifying the controller, it didn't work.
This all makes sense, but I am still wondering what will happen when I have two Presto-Hive workers...
Add more line to etc/catalog/hive.properties
"hive.config.resources=/etc/hadoop/conf/core-site.xml,/etc/hadoop/conf/hdfs-site.xml"
ofcourse check values of path before do it.
presto-metastore.db.filename= <- is this the value for Hive Warehouse
Directory ?
=> this presto's metastore,not hive.
I just figured out what was wrong in my case:
you also have to add following line to $HIVE_HOME/conf/hive-env.sh for informing hive to open thrift port(same as mentioned under hive.metastore.uris property in hive-site.xml file). This port is used by hive client to connect to Metastore through RPC.
export METASTORE_PORT=9084
in the hive-env.sh file in the conf folder.
This should sync your hive with presto.
Related
I am creating a metastore in azure databricks for azure sql.I have given below commands to cluster config using 7.3 runtime. As mentioned in the documentation
https://learn.microsoft.com/en-us/azure/databricks/data/metastores/external-hive-metastore#spark-options
spark.hadoop.javax.jdo.option.ConnectionDriverName com.microsoft.sqlserver.jdbc.SQLServerDriver
spark.hadoop.javax.jdo.option.ConnectionURL jdbc:sqlserver://xxx.database.windows.net:1433;database=hivemetastore
spark.hadoop.javax.jdo.option.ConnectionUserName xxxx
datanucleus.fixedDatastore false
spark.hadoop.javax.jdo.option.ConnectionPassword xxxx
datanucleus.autoCreateSchema true
spark.sql.hive.metastore.jars builtin
spark.sql.hive.metastore.version 1.2.1
hive.metastore.schema.verification.record.version false
hive.metastore.schema.verification false
--
After this when I tried to create database metastore I will get cancelled automatically.
Error I am getting in Data section in databricks which I am not able to copy also.
Cluster setting
Command
--Update
According to the error message updated in the comments
The maximum length allowed is 8000, when the the length specified in declaring a VARCHAR column.
WorkAround: Use either VARCHAR(8000) or VARCHAR(MAX) for column 'PARAM_VALUE'. I would prefer using nvarchar(max), cause an nvarchar (MAX) can store up to 2GB of characters.
Apparently found an official record of the know issue!
See Error in CREATE TABLE with external Hive metastore
This is a known issue with MySQL 8.0 when the default charset is
utfmb4.
Try running this to confirm
SELECT default_character_set_name FROM information_schema.SCHEMATA S WHERE schema_name = "<database-name>"
If yes, Refer Solution
You need to update or recreate the database and set the charset to
latin1.
You have 2 options:
Manually run create statements in the Hive database with DEFAULT CHARSET=latin1 at the end of each CREATE TABLE statement.
Setup the database and user accounts. And create the database and run alter database hive character set latin1; before you launch the metastore. (This command sets the default CHARSET for the database. It is applied when the metastore creates tables.)
I am trying use Spark engine in my Hive query.
It is an old query, and I don't want to convert the whole code to a spark job.
But when I run the query, it gives following error:
Status: Failed
FAILED: Execution Error, return code 3 from org.apache.hadoop.hive.ql.exec.spark.SparkTask
The only thing I have changed is the execution engine:
set hive.execution.engine=spark;
The above change works for other similar queries. So I don't think that it's a configuration issue...
Or am I not aware that it is?
Has anybody faced this issue before?
Check the logs of the job to see the true error. Return code 1, 2 and 3 are all generic errors in both MR and Spark.
use verbose mode of beeline to run the query.
check query exeption logs, hiveserver logs, spark logs and spark webui worker logs (this often has the exact stack trace).
Try running spark in local mode.
What versions of hive, spark, hadoop do u use?
execute below command in hive client with hiveserver2 jdbc connection:
set hive.auto.convert.join=false;
It works for me.
Here is detail reason: https://www.cnblogs.com/CYan521/p/16716361.html
I am trying to insert data into sql server using spark using the below Jdbc methods.
Option 1:
prop.put("driver", "com.microsoft.sqlserver.jdbc.SQLServerDriver")
dataf.write.mode(org.apache.spark.sql.SaveMode.Append).jdbc(url,table_name, prop)
Table is already created. Appending new data.Job Error-ed with the below exception
Exception in thread "main"
com.microsoft.sqlserver.jdbc.SQLServerException: CREATE TABLE
permission denied in database
Question is : Why create table permission is required for appending the data?
Option2:
prop.put("driver", "com.microsoft.sqlserver.jdbc.SQLServerDriver")
org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils.saveTable(dataf, url, table_name, prop)
Above command working from spark-shell. when the same is used in scala code and packaged with dependencies giving below exception
Exception in thread "main" java.sql.SQLException: No suitable driver
at java.sql.DriverManager.getDriver(DriverManager.java:315)
I tried setting driver class-path and executor class-path and also --jars still no luck. Included sqljdbc4.jar in driver-classpath and --jars.
Copied sqljdbc4.jar to all worker nodes as well still no luck.
Any Ideas on this?
After Lot of searching and Testing, I found the answer. It might be useful for someone.
Option 1: This is because of bug in spark 1.5.X. the same was resolved
in 1.6.x and later. Because of the bug, It always try to create a new
table.
Option2: This causes because , driver name on classpath given
priority than properties we are passing as argument. Workaround for
this is to create connection and then invoke savetable.
workaround if you are using spark 1.5.x or lower.
JdbcUtils.createConnection(url, prop)
JdbcUtils.saveTable()
I'm trying to configure SSL for the Cassandra Spark connector, but I couldn't find an example of how to do it.
I'm trying to configure it like this:
SparkConf conf = new SparkConf().setAppName("someApp")
.set("spark.cassandra.connection.host", "111.111.111.111")
.set("spark.cassandra.connection.ssl.enabled", "true")
.set("spark.cassandra.connection.ssl.trustStore.path", "/some/tfile.jks")
.set("spark.cassandra.connection.ssl.trustStore.password", "apassword")
.set("spark.cassandra.connection.ssl.trustStore.type", "JKS")
.set("spark.cassandra.connection.ssl.enabledAlgorithms", "TLS_RSA_WITH_AES_128_CBC_SHA,TLS_RSA_WITH_AES_256_CBC_SHA")
.set("spark.cassandra.connection.ssl.keyStore.path", "/some/kfile.jks")
.set("spark.cassandra.connection.ssl.keyStore.password", "anotherpassword")
.set("spark.cassandra.connection.ssl.keyStore.type", "JKS")
.set("spark.cassandra.connection.ssl.protocol", "TLS");
When I try to submit the spark job, I get these errors:
Exception in thread "main" com.datastax.spark.connector.util.ConfigCheck$ConnectorConfigurationException: Invalid Config Variables
Only known spark.cassandra.* variables are allowed when using the Spark Cassandra Connector.
spark.cassandra.connection.ssl.keyStore.password is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.enabled is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.protocol is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.keyStore.type is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.trustStore.path is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.enabledAlgorithms is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.keyStore.path is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.trustStore.password is not a valid Spark Cassandra Connector variable.
No likely matches found.
spark.cassandra.connection.ssl.trustStore.type is not a valid Spark Cassandra Connector variable.
No likely matches found.
So I'm not sure if this is supported or I'm just using the wrong property names.
I saw this ticket for release 1.2.3 of the connector, but I couldn't find an example of how to use it and it sounded like it may not support keystores. I'm using version 1.4.0-M1 of the connector.
Can anyone show me an example of how to configure SSL for the Spark Cassandra connector? Thanks.
Though I don't see any keystore configurations, I can see below config variables and they are working fine for me.
Note: I am using 1.5.0-M1 version. Not sure if there is any other bug in the version you are using.
sparkConf.set("spark.cassandra.connection.ssl.enabled", "true");
sparkConf.set("spark.cassandra.connection.ssl.trustStore.password", "password");
sparkConf.set("spark.cassandra.connection.ssl.trustStore.path", "jks file path");
First, I have bought the new O'Reilly Spark book and tried those Cassandra setup instructions. I've also found other stackoverflow posts and various posts and guides over the web. None of them work as-is. Below is as far as I could get.
This is a test with only a handful of records of dummy test data. I am running the most recent Cassandra 2.0.7 Virtual Box VM provided by plasetcassandra.org linked from the main Cassandra project page.
I downloaded Spark 1.2.1 source and got the latest Cassandra Connector code from github and built both against Scala 2.11. I have JDK 1.8.0_40 and Scala 2.11.6 setup on Mac OS 10.10.2.
I run the spark shell with the cassandra connector loaded:
bin/spark-shell --driver-class-path ../spark-cassandra-connector/spark-cassandra-connector/target/scala-2.11/spark-cassandra-connector-assembly-1.2.0-SNAPSHOT.jar
Then I do what should be a simple row count type test on a test table of four records:
import com.datastax.spark.connector._
sc.stop
val conf = new org.apache.spark.SparkConf(true).set("spark.cassandra.connection.host", "192.168.56.101")
val sc = new org.apache.spark.SparkContext(conf)
val table = sc.cassandraTable("mykeyspace", "playlists")
table.count
I get the following error. What is confusing is that it is getting errors trying to find Cassandra at 127.0.0.1, but it also recognizes the host name that I configured which is 192.168.56.101.
15/03/16 15:56:54 INFO Cluster: New Cassandra host /192.168.56.101:9042 added
15/03/16 15:56:54 INFO CassandraConnector: Connected to Cassandra cluster: Cluster on a Stick
15/03/16 15:56:54 ERROR ServerSideTokenRangeSplitter: Failure while fetching splits from Cassandra
java.io.IOException: Failed to open thrift connection to Cassandra at 127.0.0.1:9160
<snip>
java.io.IOException: Failed to fetch splits of TokenRange(0,0,Set(CassandraNode(/127.0.0.1,/127.0.0.1)),None) from all endpoints: CassandraNode(/127.0.0.1,/127.0.0.1)
BTW, I can also use a configuration file at conf/spark-defaults.conf to do the above without having to close/recreate a spark context or pass in the --driver-clas-path argument. I ultimately hit the same error though, and the above steps seem easier to communicate in this post.
Any ideas?
Check the rpc_address config in your cassandra.yaml file on your cassandra node. It's likely that the spark connector is using that value from the system.local/system.peers tables and it may be set to 127.0.0.1 in your cassandra.yaml.
The spark connector uses thrift to get token range splits from cassandra. Eventually I'm betting this will be replaced as C* 2.1.4 has a new table called system.size_estimates (CASSANDRA-7688). It looks like it's getting the host metadata to find the nearest host and then making the query using thrift on port 9160.