ACADGILD ACADGILD INTRODUCTION This blog focuses on providing in depth information of Avro in Hive. Here we have discussed about the importance and necessity of Avro and how to implement it in Hive. Through this blog you will get a clear idea about Avro and its implementation in your Hadoop projects. What is Avro? Avro is one of the preferred data serialization system because of its language neutrality. Due to lack of language portability in hadoop writable classes , avro becomes a natural choice because of its ability to handle multiple data formats which can be further processed by multiple languages. Avro is also very much preferred for serializing the data in Hadoop. It uses JSON for defining data types and protocols,and serializes data in a compact binary format. Its primary use is in Apache Hadoop, where it can provide both a serialization format for persistent data, and a wire format for communication between Hadoop nodes, and from client programs to the Hadoop services. By this we can define Avro as a file format introduced with Hadoop to store data in a predefined format.This file format can be used in any of the Hadoop's tools like Pig and Hive. Implementing Avro file format in Hive Before we take a look at how the Avro file format is implemented, let’s have a quick introduction to the Avro schema and how to create Avro records, Hive tables and much more. Avro Schema Avro relies on schema. When Avro data is read, the schema used for writing it is always present. This permits each datum to be written with no per-value overheads, making serialization both fast and small. This also facilitates using it with dynamic scripting languages, since data together with its schema, is fully self-describing. When Avro data is stored in a file, its schema is stored with it, so that files may be processed later by any program. If the program reading the data expects a different schema this can be easily resolved, since both schemas are present. When Avro is used in RPC, the client and server exchange schemas in the connection handshake. (This can be optimized so that, for most calls, no schemas are actually transmitted.) Since both client and server both have the other's full schema, correspondence between same named fields, missing fields, extra fields, etc. can all be easily resolved. https://acadgild.com/blog/avro-in-hive/
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ACADGILDACADGILD
INTRODUCTION
This blog focuses on providing in depth information of Avro in Hive. Here we have discussed about the
importance and necessity of Avro and how to implement it in Hive. Through this blog you will get a
clear idea about Avro and its implementation in your Hadoop projects.
What is Avro?
Avro is one of the preferred data serialization system because of its language neutrality.
Due to lack of language portability in hadoop writable classes , avro becomes a natural choice because
of its ability to handle multiple data formats which can be further processed by multiple languages.
Avro is also very much preferred for serializing the data in Hadoop.
It uses JSON for defining data types and protocols,and serializes data in a compact binary format. Its
primary use is in Apache Hadoop, where it can provide both a serialization format for persistent data,
and a wire format for communication between Hadoop nodes, and from client programs to the Hadoop
services.
By this we can define Avro as a file format introduced with Hadoop to store data in a predefined
format.This file format can be used in any of the Hadoop's tools like Pig and Hive.
Implementing Avro file format in Hive
Before we take a look at how the Avro file format is implemented, let’s have a quick introduction to the
Avro schema and how to create Avro records, Hive tables and much more.
Avro Schema
Avro relies on schema. When Avro data is read, the schema used for writing it is always present. This
permits each datum to be written with no per-value overheads, making serialization both fast and small.
This also facilitates using it with dynamic scripting languages, since data together with its schema, is
fully self-describing.
When Avro data is stored in a file, its schema is stored with it, so that files may be processed later by
any program. If the program reading the data expects a different schema this can be easily resolved,
since both schemas are present.
When Avro is used in RPC, the client and server exchange schemas in the connection handshake. (This
can be optimized so that, for most calls, no schemas are actually transmitted.) Since both client and
server both have the other's full schema, correspondence between same named fields, missing fields,
extra fields, etc. can all be easily resolved.
https://acadgild.com/blog/avro-in-hive/
ACADGILDACADGILD
Avro schemas are defined with JSON . This facilitates implementation in languages that already have
JSON libraries.Using Avro, we can convert unstructured and semi-structured data into proper structured
data using its schemas.
Creating a table to store the data in avro format
This process is initiated with the creation of JSON based schema to serialize data in a format that has a
schema built in.
Avro has its own parser to return the provided schema as an object.
The created object allows us to create records with that schema.
We can create our schema inside the table properties while creating a Hive table