Skip to main content

Using MySQL as a Hive backend database

Listen:

Hive let us use a SQL like (HiveQL) style to analyse large datasets with ad-hoc queries, and comes as a service on top of hdfs. It is easy to use and most SQL programmers can instant write some queries.
The lack of the installation are the included derby DB, which is running on the node locally. For that Hive is not really multiuser-capable.

To use Hive with more than one user you have to setup a backend database. The database will hold all metainformations regarding your tables, partitions, splits and rows. For that the database should be safe (maybe replication) or a HVA installation. I use 2 MySQL servers in a ESX Cluster environment with enabled binary logs (Active/Standby).

Setup a server and install mysql-server version 5.1 and up. To get absolute safe you can setup a MySQL cluster ;) Let us configure the mysql-database:

# cat /etc/my.cnf
[mysqld_safe]
socket      = /var/lib/mysql/mysql.sock
[mysqld]
user        = mysql
pid-file    = /var/run/mysqld/mysqld.pid
socket      = /var/lib/mysql/mysql.sock
log-error   = /var/log/mysqld.log
datadir     = /opt/hadoop/mysql
default-storage_engine  = InnoDB
skip-bdb                = 1
old_passwords           = 0
skip_name_resolve
connect_timeout     = 30
wait_timeout        = 30
interactive_timeout = 100
key_buffer          = 128M
thread_concurrency  = 4
thread_cache        = 16
thread_stack        = 256K
table_cache         = 512
tmp_table_size      = 64M
max_heap_table_size = 64M
server-id           = 1001
log_bin             = /var/log/mysql/mysqlserver-bin.log
expire_logs_days    = 3
max_binlog_size     = 256M
innodb_file_per_table           = 1
innodb_data_file_path           = ibdata1:10M:autoextend
innodb_buffer_pool_size         = 512M
innodb_log_file_size            = 16M
innodb_flush_log_at_trx_commit  = 1
long_query_time     = 2
log_slow_queries    = /var/log/mysql/mysql-slow.log
query_cache_size    = 64M
query_cache_type    = 1
query_cache_limit   = 4M

You see, we use binary logging, as engine InnoDB (no locking tables) and some RAM related params. Consult the mysql manual for a complete description (would be a larger post).

Restart your mysqlserver (service mysqld restart). Now create the user and table:
mysql> CREATE USER 'USER'@'%' IDENTIFIED BY 'PASSWORD';
mysql> GRANT ALL PRIVILEGES ON *.* TO 'USER'@'%' WITH GRANT OPTION;
mysql> create DATABASE hive-live;

Thats all. I know, very low security. But the server should only hold one database.

Setup hive to use the metastore and add in hive-default.xml:
# vi /etc/hive/conf/hive-default.xml
<!-- add mysql metastore -->
<property>
  <name>javax.jdo.option.ConnectionURL</name>
  <value>jdbc:mysql://mysqlserver:3306/hive_live_new?createDatabaseIfNotExist=true</value>
</property>
<property>
  <name>javax.jdo.option.ConnectionDriverName</name>
  <value>com.mysql.jdbc.Driver</value>
</property>
<property>
  <name>javax.jdo.option.ConnectionUserName</name>
  <value>USER</value>
</property>
<property>
  <name>javax.jdo.option.ConnectionPassword</name>
  <value>PASSWORD</value>
</property>
<!-- end -->

To get Hive running with the mysql-driver, you have to download them [1] and copy across your cluster:
for i in $(cat /etc/hadoop-0.20/conf/slaves); do scp -r /usr/lib/hive/lib/mysql-connector-java-5.1.11-bin.jar $i:/usr/lib/hive/lib/; done

Now copy the hive-config too:
for i in $(cat /etc/hadoop-0.20/conf/slaves); do scp -r /etc/hive/conf/hive-* $i:/etc/hive/conf.dist/; done

Thats all. If you use hive now the first statement should take a while, because hive will create the schema at the backend. Let us check:

mysql> use hive_live;
mysql> show tables;
+-------------------------+
| Tables_in_hive_live     |
+-------------------------+
| BUCKETING_COLS          |
| COLUMNS                 |
| DATABASE_PARAMS         |
| DBS                     |
| DB_PRIVS                |
| GLOBAL_PRIVS            |
| IDXS                    |
| INDEX_PARAMS            |
| PARTITIONS              |
| PARTITION_KEYS          |
| PARTITION_KEY_VALS      |
| PARTITION_PARAMS        |
| PART_COL_PRIVS          |
| PART_PRIVS              |
| ROLES                   |
| ROLE_MAP                |
| SDS                     |
| SD_PARAMS               |
| SEQUENCE_TABLE          |
| SERDES                  |
| SERDE_PARAMS            |
| SORT_COLS               |
| TABLE_PARAMS            |
| TBLS                    |
| TBL_COL_PRIVS           |
| TBL_PRIVS               |
| TYPES                   |
| TYPE_FIELDS             |
+-------------------------+
28 rows in set (0.01 sec)

If you have to ugrade the schema (hive-update maybe), don't be worry. Hive comes with SQL-statements, take a look at /var/lib/hive/metastore/scripts/upgrade/mysql/. To apply a schema cd into the directory and open mysql-cli:
mysql> source /var/lib/hive/metastore/scripts/upgrade/mysql/hive-schema-0.7.0.mysql.sql <enter>

The script will load all sql-statements they will need to upgrade the database. A good idea is a full DB backup before.


[1] http://dev.mysql.com/downloads/connector/j/

Comments

Popular posts from this blog

Deal with corrupted messages in Apache Kafka

Under some strange circumstances it can happen that a message in a Kafka topic is corrupted. This happens often by using 3rd party frameworks together with Kafka. Additionally, Kafka < 0.9 has no lock at Log.read() at the consumer read level, but has a lock on Log.write(). This can cause a rare race condition, as described in KAKFA-2477 [1]. Probably a log entry looks like: ERROR Error processing message, stopping consumer: (kafka.tools.ConsoleConsumer$) kafka.message.InvalidMessageException: Message is corrupt (stored crc = xxxxxxxxxx, computed crc = yyyyyyyyyy Kafka-Tools Kafka stores the offset of every consumer in Zookeeper. To read out the offsets, Kafka provides handy tools [2]. But also zkCli.sh can be used, at least to display the consumer and the stored offsets. First we need to find the consumer for a topic (> Kafka 0.9): bin/kafka-consumer-groups.sh --zookeeper management01:2181 --describe --group test Prior to Kafka 0.9 the only possibility to get this inform

Hive query shows ERROR "too many counters"

A hive job face the odd " Too many counters:"  like Ended Job = job_xxxxxx with exception 'org.apache.hadoop.mapreduce.counters.LimitExceededException(Too many counters: 201 max=200)' FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.MapRedTask Intercepting System.exit(1) These happens when operators are used in queries ( Hive Operators ). Hive creates 4 counters per operator, max upto 1000, plus a few additional counters like file read/write, partitions and tables. Hence the number of counter required is going to be dependent upon the query.  To avoid such exception, configure " mapreduce.job.counters.max " in mapreduce-site.xml to a value above 1000. Hive will fail when he is hitting the 1k counts, but other MR jobs not. A number around 1120 should be a good choice. Using " EXPLAIN EXTENDED " and " grep -ri operators | wc -l " print out the used numbers of operators. Use this value to tweak the MR s

Life hacks for your startup with OpenAI and Bard prompts

OpenAI and Bard   are the most used GenAI tools today; the first one has a massive Microsoft investment, and the other one is an experiment from Google. But did you know that you can also use them to optimize and hack your startup?  For startups, reating pitch scripts, sales emails, and elevator pitches with one (or both) of them helps you not only save time but also validate your marketing and wording. Curios? Here a few prompt hacks for startups to create / improve / validate buyer personas, your startups mission / vision statements, and USP definitions. First Step: Introduce yourself and your startup Introduce yourself, your startup, your website, your idea, your position, and in a few words what you are doing to the chatbot: Prompt : I'm NAME and our startup NAME, with website URL, is doing WHATEVER. With PRODUCT NAME, we aim to change or disrupt INDUSTRY. Bard is able to pull information from your website. I'm not sure if ChatGPT can do that, though. But nevertheless, now