Category Archives: Impala

Hadoop Ecosystem Components contd…(Tutorial Day 5)

So continuing the old post, vendors that provide Hadoop-based platforms include Cloudera, Hortonworks, MapR, Greenplum, IBM, and Amazon. Here we will discuss more components of Hadoop ecosystem.

Data Access Components of Hadoop Ecosystem

  • Pig-

Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. Apache Pig is a tool developed by Yahoo for analyzing huge data sets efficiently and easily. The high level data flow language for this platform is called Pig Latin. Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.

At the present time, Pig’s infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist (e.g., the Hadoop subproject). Pig’s language layer currently consists of a textual language called Pig Latin.Pig is an open source project under the Apache Software Foundation, so you can learn about it online

Pig Latin is basically used it to construct dataflows, to have a scheduled job to periodically crunch the massive data from HDFS and transfer the summarized data into a relational database for reporting, & ad-hoc analyses. Hive is used for simple ad-hoc analytical queries for the data in HDFS, as Hive queries are a lot faster to write for those types of queries. Its generally used by Yahoo, Twitter etc to process web logs,images,maps etc.

Usage of Apache Pig:

  • Using Pig Latin, programmers can perform MapReduce tasks easily without having to type complex codes in Java, as it uses multi-query approach, thereby reducing the length of codes. For example, an operation that would require you to type 200 lines of code (LoC) in Java can be easily done by typing as less as just 10 LoC in Apache Pig. Ultimately Apache Pig reduces the development time by almost 16 times.
  • Pig Latin is SQL-like language and it is easy to learn Apache Pig when you are familiar with SQL.
  • Apache Pig provides many built-in operators to support data operations like joins, filters, ordering, etc. In addition, it also provides nested data types like tuples, bags, and maps that are missing from MapReduce.

Pig Use Case-

I am hereby using one of my fav use case of PIG Latin language, you can read here on Slideshare:

Scenario: You have a User data in one file ,website data in another. Now you want to find out the top 5 most visited pages by users of Age (18-25). For this scenario, MAp reduce program is full page length code, but in PIG Latin language its a small easily understandable code.

pig_latin-code_example
Code credit: Nick Dimiduk
  • Hive-

Hive is a Data warehouse system layer built on Hadoop. It allows to define a structure for unstructured big data and query the data using a SQL-like language called HiveQL. Its developed by Facebook & makes querying faster through indexing.

Hive Use Case-

Hive simplifies Hadoop at Facebook with the execution of 7500+ Hive jobs daily for Ad-hoc analysis, reporting and machine learning.

 

Read my next blog on more Hadoop ecosystem components (tutorial Day 6)

Hadoop Ecosystem & Architecture(Tutorial Day 4)

Like we discussed in last blog, Big Data is not just Hadoop. Similarly Hadoop is not one only monolithic thing, but is an ecosystem which consists of various  hadoop components and an amalgamation of different technologies.Like HDFS (Hadoop Distributed File System), Map Reduce, Pig, Hive,Hbase, Flume and so on.

Hadoop Ecosystem

The Hadoop platform consists of many tools but two key services are: Hadoop Distributed File System (HDFS) and the high-performance parallel data processing engine called Hadoop MapReduce.

Vendors that provide Hadoop-based platforms include Cloudera, Hortonworks, MapR, Greenplum, IBM, and Amazon.

The combination of HDFS and MapReduce provides a software framework for processing vast amounts of data in parallel on large clusters of commodity hardware (potentially scaling to thousands of nodes) in a reliable, fault-tolerant manner. We can combine various Hadoop Ecosystem tools to serve the business requirements in cost effective fashion.
Below image describes the Hadoop Ecosystem.

hadoop-ecosystem_mines

In view of Hadoop ecosystem prominence is given to Hadoop Core components (Hadoop common, YARN, HDFS and MapReduce), which we will discuss first.

1) Hadoop Common refers to the collection of common utilities ,libraries,necessary Java files and scripts that support other Hadoop modules. It is an essential part or module of the Apache Hadoop Framework.

2) Hadoop YARN is described as a clustering platform or framework that helps to manage resources and schedule tasks.It is a great enabler for dynamic resource utilization on Hadoop framework as users can run various Hadoop applications without having to bother about increasing workloads.

3) HDFS is a distributed file system that runs on standard or low-end hardware. Developed by Apache Hadoop, HDFS works like a standard distributed file system but provides better data throughput and access through the MapReduce algorithm, high fault tolerance and native support of large data sets.

HDFS comprises of 3 important components called NameNode, DataNode and Secondary NameNode. HDFS operates on a Master-Slave architecture model where the NameNode acts as the master node for keeping a track of the storage cluster and the DataNode acts as a slave node summing up to the various systems within a Hadoop cluster.

It provides data reliability by replicating each data instance as three different copies – two in one group and one in another. These copies may be replaced in the event of failure.

Default replication count is 3
• 1st replica on the local rack
• 2nd replica on the local rack but different machine
• 3rd replica on the different rack

The HDFS architecture consists of clusters, each of which is accessed through a single NameNode software tool installed on a separate machine to monitor and manage the that cluster’s file system and user access mechanism. The other machines install one instance of DataNode to manage cluster storage.
Because HDFS is written in Java, it has native support for Java application programming interfaces (API) for application integration and accessibility. It also may be accessed through standard Web browsers.

hdfs-architecture

Namenode

The namenode is the commodity hardware that stores the metadata like name of the datanodes, location or path, replica block path etc.The system having the namenode acts as the master server and there can only be 1 Name node. If you want you can create a replica of it and called it as Secondary Namenode. But either of one can be active.It does the following tasks:

  • Manages the file system namespace.
  • Regulates client’s access to files.
  • It also executes file system operations such as renaming, closing, and opening files and directories.

Datanode

A DataNode stores & manages the data stored in HDFS. In a functional filesystem we can have more than one DataNode, with data blocks replicated across them.DataNode connects to the Namenode; spinning until that service comes up. It then responds to requests from the Namenode for filesystem operations.

Client applications can talk directly to a DataNode, once the Namenode has provided the location of the data. Similarly, MapReduce operations delegated out to Task Tracker instance near a DataNode, can talk directly to the DataNode to access the files. following task are performed here:

  • Datanodes perform read-write operations on the file systems, as per client request.
  • They also perform operations such as block creation, deletion, and replication according to the instructions of the namenode.
  • DataNode instances can talk to each other, which is what they do when they are replicating data.

Block

A Hadoop block is a file on the underlying filesystem. Since the underlying filesystem stores files as blocks, one Hadoop block may consist of many blocks in the underlying file system. Blocks are large.In other words, the minimum amount of data that HDFS can read or write is called a Block. The default block size is 64MB, but it can be increased as per the need to change in HDFS configuration.Most systems run with block sizes of 128 megabytes or larger.

4) MapReduce is a programming model introduced by Google. It breaks down a big data processing job into smaller tasks. It is responsible for the analyzing large data-sets in parallel before reducing it to find the results. It is highly scaleable & has several forms of implementation provided by multiple programming languages, like Java, C# and C++.

The MapReduce executed in 2 stages :

  1. Map:  The map or mapper’s job is to process the input data. Generally the input data is in the form of file or directory and is stored in the Hadoop file system (HDFS). The input file is passed to the mapper function line by line. The mapper processes the data and creates several small chunks of data.
  2. Reduce: It is combination of Shuffle and Reduce.The Reducer’s job is to process the data that comes from the mapper. After processing, it produces a new set of output, which will be stored in the HDFS.

The main advantage of the MapReduce framework is its fault tolerance, where periodic reports from each node in the cluster are expected when work is completed.
A task is transferred from one node to another. If the master node notices that a node has been silent for a longer interval than expected, the main node performs the reassignment process to the frozen/delayed task.

As discussed above, there are several other Hadoop components that form an integral part of the Hadoop ecosystem, making Hadoop faster or developing novel features and functionalities. To know further about some of the eminent Hadoop components , please read my Next Blog.

To learn more on MapReduce Algorithm and how it works click here.

Pic/content credit :Google and Specific mentioned.

What is Big Data ? Is it Only Hadoop ? (Tutorial day 1)

Big Data, the new buzz word in the today’s technology is gaining more importance due to its high rewards. A systematic and focused approach toward the adoption of Big Data allows one to derive maximum value and utilize the power of Big Data.

 Its nothing but a new framework or system to get insight of existing different data forms and increasing the researchers/analyst power to get more out of existing system.

As BG Univ says, “Big data is about the application of new tools to do MORE analytic on MORE data for More people.”

Lifecycle of data can be defined as :

 

People get confuse with Big Data & Hadoop as 2 similar things. But no, Big data is not only Hadoop

Big Data is not a tool or single technique. Its actually a platform or a framework having various components like Data Warehouses (providing OLAP data/History), Real time Data systems and Hadoop (provides insight to structured/semi or unstructured Data).

Examples of Big Data are like Traffic data, Flights Data/ Search engine data etc.

Thus Big Data includes huge volume, high velocity, and extensible variety of data. The data in it will be of three types :

a) Structured data: Relational data.
b) Semi Structured data: XML data.
c) Unstructured data: Word, PDF, Text, Media Logs.

 Big Data can be characterized by 3 V’s :

1) Velocity -> Batch processing data, real time
2) Variety-> Structured, semi-structured, unstructured and polymorphic data
3) Volume-> Terabytes to Petabytes

Big Data puts existing traditional systems into trouble due to many reasons because when data increases the complexity, Security, maintenance, processing time of it also increases. Big Data gets Distributed processing system into picture. Its using multiple system/disk for parallel processing.

There are various tools & technologies in the market from different vendors including IBM, Microsoft, etc., to handle big data. Few of them are:

1) No SQL Big Data systems are designed to provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored. It allows massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement. For example MongoDB
2)MPP & MapReduce provide analytical capabilities for complex analysis including lot of data. Based on them we have Hadoop, Hive, Pig, Impala
3) Storage (HDFS ie Hadoop Distributed File System)
4) Servers (Google App Engine)
There are major challenges with Big Data.

Read  Day 2 tutorial to understand further and bookmark this page for future reference.

What are the difference between Hive / Impala & Pig

Comparing Impala to Hive & Pig

Similarities:
  • Queries expressed in high-level languages
  • Alternatives to writing map-reduce code
  • Used to analyze data stored on Hadoop cluster

Differences:

Impala  

It was created based on Google’s Dremel paper.
1) It is an interactive SQL like query engine that runs on top of Hadoop Distributed File System (HDFS).
2) Its an open source massively parallel processing (MPP) query engine on top of clustered systems like Apache Hadoop.
3) MPP style parallel databases have a relation model, more suitable for processing structured and semi-structured data. Due to its architectural advantages, it doesn’t involve the overheads of a MapReduce jobs viz. job setup and creation, slot assignment, split creation, map generation etc., hence enables low-latency.
4) It offers lower latency / processing time for the queries at the cost of less scalability and less stability.
5) Impala supports high-performance UDF (User Defined Function) written in C++, as well as reusing some Java-based Hive UDFs.
6) Impala does not return column overflows as NULL, so that customers can distinguish between NULL data and overflow conditions similar to how they do so with traditional database systems.
7) Impala does not store or interpret timestamps using the local timezone, to avoid undesired results from unexpected time zone issues. Timestamps are stored and interpreted relative to UTC.
8) Impala utilizes the Apache Sentry authorization framework for Security, which provides fine-grained role-based access control to protect data against unauthorized access or tampering.
9) It can query data stored in HDFS or HBase tables
10) Uses subset of SQL 92 and do not support Stored Procedure
11) The Impala TIMESTAMP type can represent dates ranging from 1400-01-01 to 9999-12-31.  
12) With Impala, you can query the following File formats:Parquet /Avro /RCFile /SequenceFile
 /Unstructured text
13)  Impala shares the meta store with Hive
14) Impala can process in milliseconds when running at low load conditions and Impala is one of the valid choices if no SQL parallel processing is executed.
15) Impala is an MPP-like engine, so each query you are executing on it will start executor on each and every node of your cluster. This delivers the best performance for a single query running on the cluster, but the total throughput degrades heavily under high concurrency. In such systems you should limit the amount of parallel queries to kinda low value of ~10.

Being highly used it still has cons like:

1)  Impala can’t handle complex data types(Array,Map or Struct)

2)  Impala is not fault tolerant For e.g. if you run a query in Impala and if the query fails you will have to start the query all over again
3) Doesnot not support Parameters in scripts

4) Impala does not currently support many of HiveQL statements like ,ANALYZE TABLE (the Impala equivalent is COMPUTE STATS),DESCRIBE COLUMN,DESCRIBE DATABASE,EXPORT TABLE,IMPORT TABLE, many more
5) Impala does not implicitly cast between string and numeric or Boolean types. Always use CAST() for these conversions.
6) Impala does perform implicit casts among the numeric types, when going from a smaller or less precise type to a larger or more precise one. For example, Impala will implicitly convert a SMALLINT to a BIGINT or FLOAT, but to convert from DOUBLE to FLOAT or INT to TINYINT requires a call to CAST() in the query.
7) Impala does perform implicit casts from string to timestamp. Impala has a restricted set of literal formats for the TIMESTAMP data type and the from_unixtime() format string.
8) Impala, is not currently supported by YARN 
9) Impala is not the best choice if there is a batch execution, and SQL parallel execution 

Hive 

It is a component of Horton works Data Platform(HDP). 
1) Hive provides a SQL-like interface to data stored in Hadoop clusters. 
2) It translate SQL queries into MapReduce/Tez/Spark jobs and executes them on the cluster, to implement batch based processing. Hence best suited for ETL- long running queries.
3) Its used by Data Analyst for completely structured data.
4) Supports complex Data types like arrays, Struct etc, custom file formats, “DATE” data type,XML and JSON functions.
5) Its fault tolerant .For e.g. if you run a query in hive mapreduce and while the query is running one of your data-node goes down still the output is given as  query will start running mapreduce jobs in other nodes.Its fault tolerant.
6) Supports Parameters Which Can Come Handy While Writing Hive Scripts.
7)  Its supported by YARN. So you can manage your resources for mapreduce or any other applications supported by YARN
8) Hive runs on top of MapReduce/Tez framework which requests resources based on the amount of data to process. This way for large clusters it would give you much better concurrency for “small” queries, as each of them would request small amount of execution resources which would result in more queries running in parallel.
9) The Hive component included in CDH 5.1 and higher now includes Sentry-enabled security .GRANT, REVOKE, and CREATE/DROP ROLE statements. Earlier Hive releases had a privilege system with GRANT and REVOKE statements that were primarily intended to prevent accidental deletion of data, rather than a security mechanism to protect against malicious users.
10) Uses subset of SQL 92 and do not support Stored Procedure
11) Hive TIMESTAMP type can represent dates ranging from 0000-01-01 to 9999-12-31. 
12) Hive supports several file formats like Text File /SequenceFile /RCFile/ Avro Files/ORC Files
     / Parquet/ Custom INPUTFORMAT and OUTPUTFORMAT. 

But the cons are big as well – 

1) Since Hive uses MapReduce to access Hadoop clusters, query overheads results in high latency. 
2) lower performance especially for table joins
3) No query optimizer 

 Pig

Pig which is a scripting language with a focus on data flows.It has two parts:
a) A language for processing data, called Pig Latin.

b) A set of evaluation mechanisms for evaluating a Pig Latin program. Current evaluation mechanisms include (a) local evaluation in a single JVM, (b) evaluation by translation into one or more Map-Reduce jobs, executed using Hadoop

1) Pig can process data of any format, such as tab delimited text files, are supported via built-in capabilities. A user can add support for a file format by writing a function that parses the bytes of a file into objects in Pig’s data model, and vice versa.
2) Pig’s data model is similar to the relational data model.
3) In Pig, tables are called bags. Pig also has a “map” data type, which is useful in representing semi-structured data, e.g. JSON or XML.
4)  It can combine multiple data sets, via operations such as join, union or co-group, OR can split a single data set into multiple ones, using an operation called split.
5) It is a Procedural Data Flow Language and mostly used by Researchers or programmers.
6) Pig is Fault Tolerant
7) Pig supports “maps” of (key, value) pairs, where retrieving the value associated with a given key is an efficient operation. Maps provide a convenient way to represent semi-structured data, where the set of non-null fields varies from record to record. Maps are helpful when processing JSON, XML, and sparse relational data (i.e., tables with a lot of null values).