Hive tutorial pdf free download

Here, hduser is the name of user with which you have logged in and Hive Run Hive from a terminal:. Make sure that the Hive node has a connection to Hadoop cluster, which means Hive would be installed on any of the Hadoop nodes, or Hadoop configurations are available in the node's class path. This installation uses the embedded Derby database and stores the data on the local filesystem.

Only one Hive session can be open on the node. Follow these steps to configure Hive with the local metastore. Here, we are using the MySQL database as a metastore:. Here, hduser is the user name, and apache-hive In case of MySql, Hive needs the mysql-connector jar.

Create a file, hive-site. There is a known " JLine " jar conflict issue with Hadoop 2. If you are getting the error "unable to load class jline. Here, hduser is the user name and apache-hive There is a known "JLine" jar conflict issue with Hadoop 2.

Assuming that Hive has been configured in the remote metastore, let's look into how to install and configure HCatalog. The HCatalog table, which needs to be created, must have the group " mygrp ".

The HCatalog table, which needs to be created, must have permissions " rwxrwxr-x ". Tells HCatalog that myscript. Hive 0. Because we have already configured Hive, we could access the HCatalog command-line hcat command on shell. Besides the Hive metastore, Hive components could be broadly classified as Hive clients and Hive servers. Hive servers provide interfaces to make the metastore available to external applications and check for user's authorization and authentication, and Hive clients are various applications used to access and execute Hive queries on the Hadoop cluster.

Let's take a look at its various components. Hive metastore URIs start a metastore service on the specified port. The metastore service starts as a Java process in the backend. You can start the Hive metastore service with the following command:. HiveServer2 is an interface that allows clients to execute Hive queries and get the result. It also provisioned for the authentication and authorization of the user. The HiveServer2 service also starts as a Java process in the backend.

You can start HiveServer2 with the following command:. The following are the different clients available in Hive to query metastore data or to submit Hive queries to Hive servers. If you have configured HiveServer2, then a Beeline client can be used to interact with Hive. Using beeline, a connection could be made to any HiveServer2 instance with any username and password. Apache Hive is an open source framework available for compilation and modification by any user.

Hive source code is a maven project. The source has intermittent scripts executed on a UNIX platform during compilation. Although the source could also be compiled on Windows, you need to comment out the intermittent scripts execution.

Maven : The following are the steps to configure maven:. Download the Apache maven binaries for Linux. The following are the various sections included in Hive packages. Hive source consists of different modules categorized by the features they provide or as a submodule of some other module. The following is the list of Hive modules and their usage in Hive:.

This package includes the components responsible for mapping the Hive table to the accumulo table. AccumuloStorageHandler and AccumuloPredicateHandler are the main classes responsible for mapping tables. Ant is also needed to configure the Hive Web Interface server. It also provides interfaces to access HBase and Hive tables for join and union in a single query.

Metastore : This is the API that provides access to metastore entities including database, table, schema, and serdes. Serde : This module has an implementation of serializer and deserializer used by Hive to read and write data. It helps in validating and parsing record and field types. Here, we will take a quick look at the command-line debugging option in Hive. Once a debug port is attached to Hive and Hive server suspension is enabled at startup, the following steps will help you debug Hive queries:.

Explicit type conversion can be done using the cast operator as shown in the Built In Functions section below. Using the primitive types and the constructs for creating complex types, types with arbitrary levels of nesting can be created. For example, a type User may comprise of the following fields:.

Timestamps have been the source of much confusion, so we try to document the intended semantics of Hive. Java's "LocalDateTime" timestamps record a date and time as year, month, date, hour, minute, and seconds without a timezone. These timestamps always have those same values regardless of the local time zone. For example, the timestamp value of " " is decomposed into year, month, day, hour, minute and seconds fields, but with no time zone information available.

It does not correspond to any specific instant. It will always be the same value regardless of the local time zone. Unless your application uses UTC consistently, timestamp with local time zone is strongly preferred over timestamp for most applications.

When users say an event is at , it is always in reference to a certain timezone and means a point in time, rather than in an arbitrary time zone.

Java's "Instant" timestamps define a point in time that remains constant regardless of where the data is read. Thus, the timestamp will be adjusted by the local time zone to match the original point in time. The operators and functions listed below are not necessarily up to date. Hive Operators and UDFs has more current information.

The comparison is done character by character. Gives the result of adding A and B. The type of the result is the same as the common parent in the type hierarchy of the types of the operands, for example, since every integer is a float.

Gives the result of subtracting B from A. The type of the result is the same as the common parent in the type hierarchy of the types of the operands. Gives the result of multiplying A and B. Note that if the multiplication causing overflow, you will have to cast one of the operators to a type higher in the type hierarchy. Gives the result of dividing B from A. If the operands are integer types, then the result is the quotient of the division.

Gives the reminder resulting from dividing A by B. Gives the result of bitwise OR of A and B. The first element has index 0, for example, if A is an array comprising of ['foo', 'bar'] then A[0] returns 'foo' and A[1] returns 'bar'.

Specifiying the seed will make sure the generated random number sequence is deterministic. For example, concat 'foo', 'bar' results in 'foobar'. This function accepts arbitrary number of arguments and return the concatenation of all of them.

For example, substr 'foobar', 4 results in 'bar'. For example, ltrim ' foobar ' results in 'foobar '. For example, rtrim ' foobar ' results in ' foobar'. A null is returned if the conversion does not succeed. Extract json object from a json string based on json path specified, and return json string of the extracted json object. It will return null if the input json string is invalid. These operations work on tables or partitions.

These operations are:. NOTE: Many of the following examples are out of date. More up to date information can be found in the LanguageManual. The following examples highlight some salient features of the system.

See Hive Data Definition Language for detailed information about creating, showing, altering, and dropping tables. In this example, the columns of the table are specified with the corresponding types.

Comments can be attached both at the column level as well as at the table level. Additionally, the partitioned by clause defines the partitioning columns which are different from the data columns and are actually not stored with the data.

When specified in this way, the data in the files is assumed to be delimited with ASCII ctrl-A as the field delimiter and newline as the row delimiter. The field delimiter can be parametrized if the data is not in the above format as illustrated in the following example:. The row delimintor currently cannot be changed since it is not determined by Hive but Hadoop delimiters. It is also a good idea to bucket the tables on certain columns so that efficient sampling queries can be executed against the data set.

If bucketing is absent, random sampling can still be done on the table but it is not efficient as the query has to scan all the data. In the example above, the table is clustered by a hash function of userid into 32 buckets. Within each bucket the data is sorted in increasing order of viewTime. Such an organization allows the user to do efficient sampling on the clustered column — n this case userid.

The sorting property allows internal operators to take advantage of the better-known data structure while evaluating queries with greater efficiency. In this example, the columns that comprise of the table row are specified in a similar way as the definition of types.

The delimited row format specifies how the rows are stored in the hive table. In the case of the delimited format, this specifies how the fields are terminated, how the items within collections arrays or maps are terminated, and how the map keys are terminated. To list existing tables in the warehouse; there are many of these, likely more than you want to browse.

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