Hadoop File System (HDFS) is a distributed file system. Store all types of files in the Hadoop file system. It supports all standard formats such as Gifs, text, CSV, tsv, xls, etc. Beginners of Hadoop can opt for tab_delimiter (data separated by tabs) files because it is -
Easy to debug and readable
The default format of Apache Hive
What is the Data Serialization Storage format?
Storage formats are a way to define how to store information in the file. Most of the time, assume this information is from the extension of the data. Both structured and unstructured data can store on HADOOP-enabled systems. Common Hdfs file formats are -
Plain text storage
Why Storage Formats?
File format must be handy to serve complex data structures
HDFS enabled applications to find relevant data in a particular location and write back data to another location.
Dataset is large
Having storage constraints
A distributed file system that handles large data sets running on commodity hardware and used to scale a single Apache Hadoop cluster of nodes. Source: Apache Hadoop distributed file system
Why choose different File Formats?
Proper selection of file format leads to -
Faster read time
Faster write time
Splittable files (for partial data read)
Schema evolution support (modifying dataset fields)
Advance compression support
Snappy compression leads to high speed and reasonable compression/decompression.
File formats help to manage Diverse data.
Enabling Data Serialization in Hadoop
Data serialization is a process that converts structure data manually back to the original form.
Serialize to translate data structures into a stream of data. Transmit this stream of data over the network or store it in DB regardless of the system architecture.
Isn't storing information in binary form or stream of bytes is the right approach.
Serialization does the same but isn't dependent on architecture.
Consider CSV files contains a comma (,) in between data, so while Deserialization, wrong outputs may occur. Now, if metadata is stored in XML form, a self- architected form of data storage, data can easily deserialize.
Why Data Serialization for Storage Formats?
To process records faster (Time-bound).
When proper data formats need to maintain and transmit over data without schema support on another end.
Now when in the future, data without structure or format needs to process, complex Errors may occur.
Serialization offers data validation over transmission.
A part of the Big Data Architectural Layer in which components are decoupled so that analytic capabilities may begin. Click to explore about, Data Ingestion
Areas of Serialization for Storage Formats
To maintain the proper format of a data serialization system must have the following four properties -
Compact - helps in the best use of network bandwidth
Fast - reduces the performance overhead
Extensible - can match new requirements
Inter-operable - not language-specific
Serialization in Hadoop has two areas -
When a client calls a function or subroutine from one pc to the pc in-network or server, that calling is a remote procedure call.
It is better than java's inbuilt serialization as java serialization isn't compact. Serialization and Deserialization of data help maintain and manage corporate decisions for effective use of resources and data available in Data warehouse or any other database -writable - language specific to java.
Stacking of Storage Formats Hadoop
Use splittable formats
CSV files lack block compression; using CSV files in HADOOP may increase reading performance cost. Schema support is also within the limit.
Use JSON records instead of JSON files. For better performance, experiments have to perform for each use case of JSON.
AVRO file format and data serialization framework.
Sequence files are complex in reading.
Write operation is slower if RC (Row-Columnar) files are in use.
Optimized RC (ORC) files are also the option to use but have less support.
Parquet Files are using a columnar format to store data and process it.
Guide to Apache Hadoop File Format Configuration
Hadoop distribution information and architecture type (system information where Hadoop system is working and process jobs) must analyze thoroughly.
Perform Schema evolution by defining the structure of your metadata.
Processing requirements for file formats and configure the HADOOP system.
Engine support for reading and write operations.
Evaluate storage size at a production level HADOOP File formats selection.
Text files are lightweight, but splitting those files and reading data leads to huge maps (MAPS needs to create a complicated way to achieve splitting of text (files data).
Sequence files support block compression. A hive has SQL types, so not worthy of working with Hive.
RCFILE has a high compression rate, but it takes more time to load data.
ORC can reduce data size up to 75% and suitable with hive but increases CPU overhead. Serialization in ORC depends on data type (either integer or string).
AVRO provides features like serialization and Deserialization with file format also. The core of AVRO is its schema. Supports dynamic and static types.
Parquet provides the facility to partition data into both rows and columns but computationally slow on the right side.
Avro is a data exchange and data serialization service system. It is based on schemas.
Always store Schema in an AVRO file with data.
Support Specific and Generic data.
JSON defines AVRO schema (most languages have JSON libraries), and data format is binary, which determines the efficiency and makes it compact.
Set the default schema for some mismatched or failed schema.
At the same time, a few questions arise that - Is it possible to handle any schema change
Yes, in AVRO, schema changes like missing fields, changed/modified fields, and added/new areas are easy to maintain What will happen to past data when there is a change in schema.
AVRO can read data with a new schema regardless of data generation time. Is there any problem that occurs while trying to convert AVRO data to other formats, if needed.
There are some inbuilt specifications in the Hadoop system while using a Hybrid system of data? In such a way, Apache spark can convert Avro files to parquet. Is it possible to manage MapReduce jobs.
MapReduce jobs can efficiently achieve by using Avro file processing in MapReduce itself. What connection Handshake do.
Connection handshake helps to exchange schemas between client and server during RPC.
How is AVRO different from other systems? (Thrift and protocol buffer)
AVRO has Dynamic Schema stores for serialized values in binary format in a space-efficient way.
JSON schema and data in the file.
Parser No compilation directly by using the parser library.
AVRO is a language-neutral system.
How to implement Avro?
Have the schema of data format ready
To read schema in program
Compile using AVRO (by generating class)
Direct read via Parser library
Achieve serialization through serialization API (java)
By creating class - A schema fed to AVRO utility and then gets processes as a java file. Now write API methods to serialize data.
By parser library - Fed schema to AVRO utility and access serialization parser library to serialize data. Achieve Deserialization through deserialization API (java.
By generating class - Deserialize the object and instantiate DataFileReader class.
By parser library - Instantiate parser class.
Creating schema in Avro
There are three ways to define and create JSON schemas of AVRO -
The JSON object
There are some data type that needs to mention in schema -
Primitive (common data types)
Complex (when collective data elements need to be process and store)
Primitive data types contain null, boolean, int, long, bytes, float, string, and double. In contrast, complex contains Records (attribute encapsulation), Enums, Arrays, Maps (associativity), Unions (multiple types for one field), and Fixed (deals with the size of data).
Data types help to maintain sort order.
Logical types (a super form of complex data types) are used to store individual data involved, i.e., time, date, timestamp, and decimal.
Apache Avro Use Cases
Apture is using AVRO for logging matrices data.
RichRelevance is using binary formatted key/value pairs.
There are various types of testing in Big Data projects such as Database testing, Infrastructure, Performance Testing, and Functional testing. Click to explore about, Big Data Testing Best Practices
What is Apache Parquet?
Apache developed parquet, and it is a columnar storage format for the Hadoop ecosystem. It supports all data processing frameworks. Also, it supports all data models or programming languages -
Provides a compressed and efficient representation of columnar data.
Record stripping and assembly algorithm from Dremel paper.
Compression schemes can be present at the per-column level.
Metadata is for single-pass writing after data.
The file format contains column-wise data, which is split into row groups.
Associations with data are done in three types of metadata forms
Files -Includes associations of schema element, principal value, and row group, which connect further to column chunk. Column (chunks) - Contains associations with KeyValue and connected with every ColumnChunk under RowGroup. Page Header - Contains info of size and associations of PageDataHeader, indexPage
Header and Dictionary PageHeader
Had a capability to reuse efficient encoding over primitive types utilizing Annotations that further can decode and interpret and Support many engines such as Hive, Impala, spark, etc.
Apache Parquet Capabilities
Task-oriented serialization of data can be performed if some column-based decisions need to be managed.
Data in 3 rows like r1(c1,c2,c3), r2(c1,c2,c3), r3(c1,c2,c3) will represent in columnar storage as 3 columns like c1(r1,r2,r3), c2(r1,r2,r3), c3(r1,r2,r3).
Easy to split data partition in column way.
As data is compressed and metadata is associated, modifications and new additions to schema may reflect changes immediately without interruption.
The columnar format can store the same primitive types of values together, forming a nested data structure (using Definition and repetition levels).
Columnar storage provides more homogeneous data so that the compression rate will be better for the Hadoop cluster.
ProtoBuf model can help to describe the data structure of the columnar format using a schema.
Encoding and decoding of column data make data more compact and compressed in nature.
Causes of error in Parquet.
When any metadata is corrupt, then it will cause particular associative data to lose.
While writing file metadata at the end of the file may also cause data loss if any error occurs.
When the structure of the external table is changed, how can metadata information be updated?.
Apache Parquet Solution Offerings
The approach with sync markers in RC and AVRO is usable here. Write file metadata at some Nth RowGroup, so during error, partially written data can be recovered.
Easy to integrate with AVRO, thrift, etc. Models.
When the PURGE option is not provided, managed tables can be retrieved from trash for a defined duration.
Interactive shell with essential Linux commands support
Beeline CLI, a JDBC client on SQLLine (used at HiveServer2)
Use AvroSerde for Avro data in HIVE tables, HiveQL for ORC file formats
Hive does not support some data types of AVRO, but AvroSerde automatically do the work for this
Test cases of Various file formats in Hadoop
Test case outputs are directly or indirectly changed with test cases, data model usage, and hardware specification and configuration.
Following are some test cases (based on CERN's blog) of Kudu, AVRO, Parquet, HBase -
Based on space utilization
Kudu and Parquet serve the best compression size with Snappy and GZip algorithms.
Based on the ingestion rate
AVRO and Parquet are best as compared with the writing speed of others
Based on Lookup latency of random data
Kudu and Hbase are faster than others
The scan rate of data
AVRO has the best scan rate of data when non-key columns are in output Test Cases Output
Using the hybrid approach in the Hadoop file system provides more efficient and Fast data processing with Structured or unstructured data for SerDes and Analytics on Data. The following are some useful points with some formats that use within the Hybrid approach.
When used with snappy compression, parquet or kudu reduces the data volume by ten times than simple uncompressed serialization format.
Random lookup operations case is best suitable for HBase or Kudu. Parquet is also fast, but resources use more.
Analytic on data is the best use case for Parquet and Kudu
HBase and Kudu can alter in-place data
Avro is the quickest encoder for structured data. Best when all attributes of record are necessary.
Parquet works seamlessly for scan and ingestion
SerDe in Apache Hive
Hive has some built-in SerDe for Avro, ORC, parquet, CSV, JsonSerDe, and Thrift. Custom support is also available for users. Hive engines automatically handle object conversion. Two types of processing are done for serialization and Deserialization of records. Input Processing
The Execution Engine uses the configured input format to read
Then performs Deserialization
that engine also holds object inspector
Deserialized objects and inspectors are then passed to operators by the engine to manipulate records.
Serialize method of Serde uses deserialized object and object inspector
Then performs serialization on it in expected output format for writing
A Holistic Approach
To know more about how Why Adopting Hadoop Storage Format Matters and understanding how to process records faster, we recommend talking to our expert.