What is Apache Avro?
Ans. An open source project which offers data serialization as well as data exchange services for Apache Hadoop is what we call Apache Avro. It is possible to use these services together or independently both. However, programs can efficiently serialize data into files or into messages, with the serialization service. In addition, data storage is very compact and efficient in Avo because here data definition is in JSON, so, data itself is stored in the binary format making it compact and efficient.
Name some Complex types of Data types, Avro Supports.
Ans. There are six kinds of complex types which Avro supports :
Records
Enums
Arrays
Maps
unions
fixed
What are best features of Apache Avro?
Ans. Some of the best features of Avro are:
Schema evolution
Untagged data
language support
transparent compression
Dynamic typing
native support in MapReduce
Rich data structures
Explain some Advantages of Avro.
Ans. Pros of Avro are:
The Smallest Size.
It Compresses block at a time; splittable.
Maintained Object structure.
Also, Supports reading old data w/ new schema.
Explain some Disadvantages of Avro.
Ans. Cons of Avro are:
It is must to use .NET 4.5, in the case of C# Avro, to make the best use of it.
Potentially slower serialization.
In order to read/write data, need a schema.
What do you mean by Schema Declaration?
Ans. In JSON, a Schema is represented by one of:
A JSON string
A JSON object:
{“type”: “typeName” …attributes…}
A JSON array
Explain the term Serialization.
Ans. To transport the data over the network or to store on some persistent storage, the process of translating data structures or objects state into binary or textual form is what we call Serialization. In other words, serialization is also knowns as marshaling and deserialization is known as unmarshalling.
What do you mean by Schema Resolution?
Ans. Whether from an RPC or a file, a reader of Avro data, can always parse that data since its schema is offered. yet it is possible that schema may not be exactly the schema what we expect so for that purpose we use Schema Resolution.
Explain Avro SASL Profile?
Ans. Basically, SASL offers a framework for authentication and security of network protocols. In Avro also we use SASL Profile for authentication and security purpose.
What is the way of creating Avro Schemas?
Ans. In the format “lightweight text-based data interchange”, JavaScript Object Notation (JSON), the Avro schema gets created. We can make it in various ways −
A JSON string
JSON object
A JSON array
State some key Points about Apache Avro.
Ans. Some key points are:
Avro is a Data serialization system
It uses JSON based schemas
Moreover, to send data, it uses RPC calls.
And, during data exchange, Schema’s sent.
What Avro offers?
Ans. Avro offers:
Avro offers Rich data structures.
And, a compact, fast, binary data format.
Further, it offers a container file, to store persistent data.
Remote procedure call (RPC).
Explain Thrift & Protocol Buffers Vs. Avro
Ans. The most competent libraries with Avro are Thrift and Protocol Buffers. The difference between them is −
As per the need, Avro supports both dynamic and static types. Basically, to specify schemas and their types, Protocol Buffers and Thrift uses Interface Definition Languages (IDLs).
As Avro is built in the Hadoop ecosystem but Thrift and Protocol Buffers are not.
Why Avro?
Ans. Some features where Avro differs from other systems, are:
Dynamic typing:
Untagged data:
No manually-assigned field IDs
How to use Avro?
Ans. The workflow to use Avro is −
We need to create schemas at first to read the schemas into our program, that is possible in two ways:
Generating a Class Corresponding to Schema
Using Parsers Library
Then perform the serialization by using serialization API provided for Avro. And then perform deserialization by using deserialization API provided for Avro.
Name some Primitive types of Data types, Avro Supports.
Ans. Avro supports a wide range of Primitive datatypes:
Null: no value
boolean: a binary value
int: 32-bit signed integer
long: 64-bit signed integer
float: single precision (32-bit) IEEE 754 floating-point number
double: double precision (64-bit) IEEE 754 floating-point number
bytes: the sequence of 8-bit unsigned bytes
string: Unicode character sequence
Name some AVRO Reference APIs.
Ans. The classes and methods which we use in the serialization, as well as deserialization of Avro schemas, are:
SpecificDatumWriter Class
SpecificDatumReader Class
DataFileWriter
Data FileReader
Class Schema.parser
Interface GenricRecord
Class GenericData.Record
When to use Avro, explain?
Ans. Mainly, for two purposes, we use Avro, like:
Data serialization
RPC (Remote procedure call) protocol
Although, some key points are:
We are able to read the data from disk with applications, by using Avro even which are written in other languages besides java or the JVM.
Also, Avro allows us to transfer data across a remote system without any overhead of java serialization.
We use Avro when we need to store the large set of data on disk, as it conserves space.
Further, by using Avro for RPC, we get a better remote data transfer.
What are the Disadvantages of Hadoop Serialization?
Ans. The only disadvantage of Hadoop Serialization is that the Writables and SequenceFiles have only a Java API. Hence to solve this issue, Avro comes in picture.
Who developed Apache Avro?
Ans. By Doug Cutting, the father of Hadoop, Apache AVRO was developed.
Who are Intended Audience to learn Avro?
Ans. Those people who want to learn the basics of Big Data Analytics by using Hadoop Framework and also those who aspire to become a successful Hadoop developer can go for Avro. Further, those aspirants who want to use Avro for data serialization and deserialization can also learn Avro.
What are Prerequisites to learn Avro?
Ans. Those who want to learn Avro must know Hadoop’s architecture and APIs, before learning Avro. Also must know Java with experience in writing basic applications before going for Avro.
Explain Avro Schemas.
Ans. Mainly, Avro heavily depends on its schema. Basically, it permits every data to be written with no prior knowledge of the schema. We can say Avro serializes fast and the data resulting after serialization is least in size with schemas.
Explain Sort Order in brief.
Ans. There is a standard sort order for data in Avro which allows data written by one system to be efficiently sorted by another system. As sort order comparisons are sometimes the most frequent per-object operation, it can be an important optimization.
What is the Advantage of Hadoop over Java Serialization?
Ans. As with the help of the Writable objects, Hadoop’s Writable-based serialization is able to reduce object-creation overhead, which is not possible with the Java’s native serialization framework that’s why using Hadoop one is an advantage.
Ans. An open source project which offers data serialization as well as data exchange services for Apache Hadoop is what we call Apache Avro. It is possible to use these services together or independently both. However, programs can efficiently serialize data into files or into messages, with the serialization service. In addition, data storage is very compact and efficient in Avo because here data definition is in JSON, so, data itself is stored in the binary format making it compact and efficient.
Name some Complex types of Data types, Avro Supports.
Ans. There are six kinds of complex types which Avro supports :
Records
Enums
Arrays
Maps
unions
fixed
What are best features of Apache Avro?
Ans. Some of the best features of Avro are:
Schema evolution
Untagged data
language support
transparent compression
Dynamic typing
native support in MapReduce
Rich data structures
 |
| Apache Avro Freshers Advanced Experienced Interview Questions and Answers |
Explain some Advantages of Avro.
Ans. Pros of Avro are:
The Smallest Size.
It Compresses block at a time; splittable.
Maintained Object structure.
Also, Supports reading old data w/ new schema.
Explain some Disadvantages of Avro.
Ans. Cons of Avro are:
It is must to use .NET 4.5, in the case of C# Avro, to make the best use of it.
Potentially slower serialization.
In order to read/write data, need a schema.
What do you mean by Schema Declaration?
Ans. In JSON, a Schema is represented by one of:
A JSON string
A JSON object:
{“type”: “typeName” …attributes…}
A JSON array
Explain the term Serialization.
Ans. To transport the data over the network or to store on some persistent storage, the process of translating data structures or objects state into binary or textual form is what we call Serialization. In other words, serialization is also knowns as marshaling and deserialization is known as unmarshalling.
What do you mean by Schema Resolution?
Ans. Whether from an RPC or a file, a reader of Avro data, can always parse that data since its schema is offered. yet it is possible that schema may not be exactly the schema what we expect so for that purpose we use Schema Resolution.
Explain Avro SASL Profile?
Ans. Basically, SASL offers a framework for authentication and security of network protocols. In Avro also we use SASL Profile for authentication and security purpose.
What is the way of creating Avro Schemas?
Ans. In the format “lightweight text-based data interchange”, JavaScript Object Notation (JSON), the Avro schema gets created. We can make it in various ways −
A JSON string
JSON object
A JSON array
State some key Points about Apache Avro.
Ans. Some key points are:
Avro is a Data serialization system
It uses JSON based schemas
Moreover, to send data, it uses RPC calls.
And, during data exchange, Schema’s sent.
What Avro offers?
Ans. Avro offers:
Avro offers Rich data structures.
And, a compact, fast, binary data format.
Further, it offers a container file, to store persistent data.
Remote procedure call (RPC).
Explain Thrift & Protocol Buffers Vs. Avro
Ans. The most competent libraries with Avro are Thrift and Protocol Buffers. The difference between them is −
As per the need, Avro supports both dynamic and static types. Basically, to specify schemas and their types, Protocol Buffers and Thrift uses Interface Definition Languages (IDLs).
As Avro is built in the Hadoop ecosystem but Thrift and Protocol Buffers are not.
Why Avro?
Ans. Some features where Avro differs from other systems, are:
Dynamic typing:
Untagged data:
No manually-assigned field IDs
How to use Avro?
Ans. The workflow to use Avro is −
We need to create schemas at first to read the schemas into our program, that is possible in two ways:
Generating a Class Corresponding to Schema
Using Parsers Library
Then perform the serialization by using serialization API provided for Avro. And then perform deserialization by using deserialization API provided for Avro.
Name some Primitive types of Data types, Avro Supports.
Ans. Avro supports a wide range of Primitive datatypes:
Null: no value
boolean: a binary value
int: 32-bit signed integer
long: 64-bit signed integer
float: single precision (32-bit) IEEE 754 floating-point number
double: double precision (64-bit) IEEE 754 floating-point number
bytes: the sequence of 8-bit unsigned bytes
string: Unicode character sequence
Ans. The classes and methods which we use in the serialization, as well as deserialization of Avro schemas, are:
SpecificDatumWriter Class
SpecificDatumReader Class
DataFileWriter
Data FileReader
Class Schema.parser
Interface GenricRecord
Class GenericData.Record
When to use Avro, explain?
Ans. Mainly, for two purposes, we use Avro, like:
Data serialization
RPC (Remote procedure call) protocol
Although, some key points are:
We are able to read the data from disk with applications, by using Avro even which are written in other languages besides java or the JVM.
Also, Avro allows us to transfer data across a remote system without any overhead of java serialization.
We use Avro when we need to store the large set of data on disk, as it conserves space.
Further, by using Avro for RPC, we get a better remote data transfer.
What are the Disadvantages of Hadoop Serialization?
Ans. The only disadvantage of Hadoop Serialization is that the Writables and SequenceFiles have only a Java API. Hence to solve this issue, Avro comes in picture.
Who developed Apache Avro?
Ans. By Doug Cutting, the father of Hadoop, Apache AVRO was developed.
Who are Intended Audience to learn Avro?
Ans. Those people who want to learn the basics of Big Data Analytics by using Hadoop Framework and also those who aspire to become a successful Hadoop developer can go for Avro. Further, those aspirants who want to use Avro for data serialization and deserialization can also learn Avro.
What are Prerequisites to learn Avro?
Ans. Those who want to learn Avro must know Hadoop’s architecture and APIs, before learning Avro. Also must know Java with experience in writing basic applications before going for Avro.
Explain Avro Schemas.
Ans. Mainly, Avro heavily depends on its schema. Basically, it permits every data to be written with no prior knowledge of the schema. We can say Avro serializes fast and the data resulting after serialization is least in size with schemas.
Explain Sort Order in brief.
Ans. There is a standard sort order for data in Avro which allows data written by one system to be efficiently sorted by another system. As sort order comparisons are sometimes the most frequent per-object operation, it can be an important optimization.
What is the Advantage of Hadoop over Java Serialization?
Ans. As with the help of the Writable objects, Hadoop’s Writable-based serialization is able to reduce object-creation overhead, which is not possible with the Java’s native serialization framework that’s why using Hadoop one is an advantage.