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I'm planning to use one of the hadoop file format for my hadoop related project. I understand parquet is efficient for column based query and avro for full scan or when we need all the columns data!
Before I proceed and choose one of the file format, I want to understand what are the disadvantages/drawbacks of one over the other. Can anyone explain it to me in simple terms?
Avro is a Row based format. If you want to retrieve the data as a whole you can use Avro
Parquet is a Column based format. If your data consists of a lot of columns but you are interested in a subset of columns then you can use Parquet
HBase is useful when frequent updating of data is involved. Avro is fast in retrieval, Parquet is much faster.
If you haven't already decided, I'd go ahead and write Avro schemas for your data. Once that's done, choosing between Avro container files and Parquet files is about as simple as swapping out e.g.,
job.setOutputFormatClass(AvroKeyOutputFormat.class);
AvroJob.setOutputKeySchema(MyAvroType.getClassSchema());
for
job.setOutputFormatClass(AvroParquetOutputFormat.class);
AvroParquetOutputFormat.setSchema(job, MyAvroType.getClassSchema());
The Parquet format does seem to be a bit more computationally intensive on the write side--e.g., requiring RAM for buffering and CPU for ordering the data etc. but it should reduce I/O, storage and transfer costs as well as make for efficient reads especially with SQL-like (e.g., Hive or SparkSQL) queries that only address a portion of the columns.
In one project, I ended up reverting from Parquet to Avro containers because the schema was too extensive and nested (being derived from some fairly hierarchical object-oriented classes) and resulted in 1000s of Parquet columns. In turn, our row groups were really wide and shallow which meant that it took forever before we could process a small number of rows in the last column of each group.
I haven't had much chance to use Parquet for more normalized/sane data yet but I understand that if used well, it allows for significant performance improvements.
Avro
Widely used as a serialization platform
Row-based, offers a compact and fast binary format
Schema is encoded on the file so the data can be untagged
Files support block compression and are splittable
Supports schema evolution
Parquet
Column-oriented binary file format
Uses the record shredding and assembly algorithm described in the Dremel paper
Each data file contains the values for a set of rows
Efficient in terms of disk I/O when specific columns need to be queried
From Choosing an HDFS data storage format- Avro vs. Parquet and more
Both Avro and Parquet are "self-describing" storage formats, meaning that both embed data, metadata information and schema when storing data in a file.
The use of either storage formats depends on the use case. Three aspects constitute the basis upon which you may choose which format will be optimal in your case:
Read/Write operation: Parquet is a column-based file format. It supports indexing. Because of that it is suitable for write-once and read-intensive, complex or analytical querying, low-latency data queries. This is generally used by end users/data scientists.
Meanwhile Avro, being a row-based file format, is best used for write-intensive operation. This is generally used by data engineers. Both support serialization and compression formats, although they do so in different ways.
Tools: Parquet is a good fit for Impala. (Impala is a Massive Parallel Processing (MPP) RDBM SQL-query engine which knows how to operate on data that resides in one or a few external storage engines.) Again Parquet lends itself well to complex/interactive querying and fast (low-latency) outputs over data in HDFS. This is supported by CDH (Cloudera Distribution Hadoop). Hadoop supports Apache's Optimized Row Columnar (ORC) formats (selections depends on the Hadoop distribution), whereas Avro is best suited to Spark processing.
Schema Evolution: Evolving a DB schema means changing the DB's structure, therefore its data, and thus its query processing. Both Parquet and Avro supports schema evolution but to a varying degree.
Parquet is good for 'append' operations, e.g. adding columns, but not for renaming columns unless 'read' is done by index.
Avro is better suited for appending, deleting and generally mutating columns than Parquet. Historically Avro has provided a richer set of schema evolution possibilities than Parquet, and although their schema evolution capabilities tend to blur, Avro still shines in that area, when compared to Parquet.
Your understanding is right. In fact, we ran into a similar situation during data migration in our DWH. We chose Parquet over Avro as the disk saving we got was almost double than what we got with AVro. Also, the query processing time was much better than Avro. But yes, our queries were based on aggregation, column based operations etc. hence Parquet was predictably a clear winner.
We are using Hive 0.12 from CDH distro. You mentioned you are running into issues with Hive+Parquet, what are those? We did not encounter any.
Silver Blaze put description nicely with an example use case and described how Parquet was the best choice for him. It makes sense to consider one over the other depending on your requirements. I am putting up a brief description of different other file formats too along with time space complexity comparison. Hope that helps.
There are a bunch of file formats that you can use in Hive. Notable mentions are AVRO, Parquet. RCFile & ORC. There are some good documents available online that you may refer to if you want to compare the performance and space utilization of these file formats. Follows some useful links that will get you going.
This Blog Post
This link from MapR [They don't discuss Parquet though]
This link from Inquidia
The above given links will get you going. I hope this answer your query.
Thanks!
I have a query about how to filter relevant records from a large data set of financial transactions. We use Oracle 11g database and one of the requirements is to produce various end-of-day reports with all sorts of criteria.
The relevant tables look roughly like this:
trade_metadata 18m rows, 10 GB
trade_economics 18m rows, 15 GB
business_event 18m rows, 11 GB
trade_business_event_link 18m rows, 3 GB
One of our reports is now taking ages to run ( > 5 hours). The underlying proc has been optimized time and again but new criteria keep getting added so we start struggling again. The proc is pretty standard - join all the tables and apply a host of where clauses (20 at the last count).
I was wondering if I have a problem large enough to consider big data solutions to get rid of this optimize-the-query game every few months. In any case, the volumes are only going up. I have read up a bit about Hadoop + HBase, Cassandra, Apache Pig etc. but being very new to this space, am a little confused about the best way to proceed.
I imagine this is not a map-reduce problem. HBase does seem to offer Filters but I am not sure about their performance. Could the enlightened folks here please answer a few questions for me:
Is the data set large enough for big data solutions (Do I need entry into the billion club first?)
If it is, would HBase be a good choice to implement this?
We are not moving away from Oracle anytime soon even though the volumes are growing steadily. Am I looking at populating the HDFS every day with a dump from the relevant tables? Or is delta write possible everyday?
Thanks very much!
Welcome to the incredibly varied big data eco-system. If your dataset size is big enough that it is taxing your ability to analyze it using traditional tools, then it is big enough for big data technologies. As you have probably seen, there are a huge number of big data tools available with many of them having overlapping capabilities.
First of all, you did not mention if you have a cluster set-up. If not, then I would suggest looking into the products by Cloudera and Hortonworks. These companies provide Hadoop distributions that include many of the most popular big data tools(hbase, spark, sqoop, etc), and make it easier to configure and manage the nodes that will make up your cluster. Both companies provide their distributions free of charge, but you will have to pay for support.
Next you will need to get your data out of Oracle and into some format in the hadoop cluster to analyze it. The tool often used to get data from a relational database and into the cluster is Sqoop. Sqoop has the ability to load your tables into HBase, Hive, and files on the Hadoop Distributed Filesystem (HDFS). Sqoop also has the ability to do incremental imports for updates instead of whole table loads. Which of these destinations you choose affects which tools you can use in the next step. HDFS is the most flexible in that you can access it from PIG, MapReduce code you write, Hive, Cloudera Impala, and others. I have found HBase to be very easy to use, but others highly recommend Hive.
An aside: There is a project called Apache Spark that is expected to be the replacement for Hadoop MapReduce. Spark claims 100x speedup compared to traditional hadoop mapreduce jobs. Many projects including Hive will run on Spark giving you the ability to do SQL-like queries on big data and get results very quickly (Blog post)
Now that your data is loaded you need to run those end of day reports. If you choose Hive, then you can reuse a lot of your sql knowledge instead of having to program Java or learn Pig Latin (not that it’s very hard). Pig Translates Pig Latin to MapReduce jobs (as does Hive’s Query Language for now), but, like Hive, Pig can target Spark as well. Regardless of which tool you choose for this step, I recommend looking into Oozie to automate the ingestion, analaytics, and movement of results back out of the cluster (sqoop export for this). Oozie allows you to schedule recurring workflows like yours so you can focus on the results not the process. The full capabilities of Oozie are documented here.
There are a crazy number of tools at your disposal, and the speed of change in this eco-system can give you whip-lash. Both cloudera and Hortonworks provide Virtual Machines you can use to try their distributions. I strongly recommend spending less time deeply researching each tool and just trying some of the them (like Hive, Pig, Oozie,...) to see what works best for your application).
What are the benefits of using either Hadoop or HBase or Hive ?
From my understanding, HBase avoids using map-reduce and has a column oriented storage on top of HDFS. Hive is a sql-like interface for Hadoop and HBase.
I would also like to know how Hive compares with Pig.
MapReduce is just a computing framework. HBase has nothing to do with it. That said, you can efficiently put or fetch data to/from HBase by writing MapReduce jobs. Alternatively you can write sequential programs using other HBase APIs, such as Java, to put or fetch the data. But we use Hadoop, HBase etc to deal with gigantic amounts of data, so that doesn't make much sense. Using normal sequential programs would be highly inefficient when your data is too huge.
Coming back to the first part of your question, Hadoop is basically 2 things: a Distributed FileSystem (HDFS) + a Computation or Processing framework (MapReduce). Like all other FS, HDFS also provides us storage, but in a fault tolerant manner with high throughput and lower risk of data loss (because of the replication). But, being a FS, HDFS lacks random read and write access. This is where HBase comes into picture. It's a distributed, scalable, big data store, modelled after Google's BigTable. It stores data as key/value pairs.
Coming to Hive. It provides us data warehousing facilities on top of an existing Hadoop cluster. Along with that it provides an SQL like interface which makes your work easier, in case you are coming from an SQL background. You can create tables in Hive and store data there. Along with that you can even map your existing HBase tables to Hive and operate on them.
While Pig is basically a dataflow language that allows us to process enormous amounts of data very easily and quickly. Pig basically has 2 parts: the Pig Interpreter and the language, PigLatin. You write Pig script in PigLatin and using Pig interpreter process them. Pig makes our life a lot easier, otherwise writing MapReduce is always not easy. In fact in some cases it can really become a pain.
I had written an article on a short comparison of different tools of the Hadoop ecosystem some time ago. It's not an in depth comparison, but a short intro to each of these tools which can help you to get started.
(Just to add on to my answer. No self promotion intended)
Both Hive and Pig queries get converted into MapReduce jobs under the hood.
HTH
I implemented a Hive Data platform recently in my firm and can speak to it in first person since I was a one man team.
Objective
To have the daily web log files collected from 350+ servers daily queryable thru some SQL like language
To replace daily aggregation data generated thru MySQL with Hive
Build Custom reports thru queries in Hive
Architecture Options
I benchmarked the following options:
Hive+HDFS
Hive+HBase - queries were too slow so I dumped this option
Design
Daily log Files were transported to HDFS
MR jobs parsed these log files and output files in HDFS
Create Hive tables with partitions and locations pointing to HDFS locations
Create Hive query scripts (call it HQL if you like as diff from SQL) that in turn ran MR jobs in the background and generated aggregation data
Put all these steps into an Oozie workflow - scheduled with Daily Oozie Coordinator
Summary
HBase is like a Map. If you know the key, you can instantly get the value. But if you want to know how many integer keys in Hbase are between 1000000 and 2000000 that is not suitable for Hbase alone.
If you have data that needs to be aggregated, rolled up, analyzed across rows then consider Hive.
Hopefully this helps.
Hive actually rocks ...I know, I have lived it for 12 months now... So does HBase...
Hadoop is a a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.
There are four main modules in Hadoop.
Hadoop Common: The common utilities that support the other Hadoop modules.
Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
Hadoop YARN: A framework for job scheduling and cluster resource management.
Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.
Before going further, Let's note that we have three different types of data.
Structured: Structured data has strong schema and schema will be checked during write & read operation. e.g. Data in RDBMS systems like Oracle, MySQL Server etc.
Unstructured: Data does not have any structure and it can be any form - Web server logs, E-Mail, Images etc.
Semi-structured: Data is not strictly structured but have some structure. e.g. XML files.
Depending on type of data to be processed, we have to choose right technology.
Some more projects, which are part of Hadoop:
HBase™: A scalable, distributed database that supports structured data storage for large tables.
Hive™: A data warehouse infrastructure that provides data summarization and ad-hoc querying.
Pig™: A high-level data-flow language and execution framework for parallel computation.
Hive Vs PIG comparison can be found at this article and my other post at this SE question.
HBASE won't replace Map Reduce. HBase is scalable distributed database & Map Reduce is programming model for distributed processing of data. Map Reduce may act on data in HBASE in processing.
You can use HIVE/HBASE for structured/semi-structured data and process it with Hadoop Map Reduce
You can use SQOOP to import structured data from traditional RDBMS database Oracle, SQL Server etc and process it with Hadoop Map Reduce
You can use FLUME for processing Un-structured data and process with Hadoop Map Reduce
Have a look at: Hadoop Use Cases.
Hive should be used for analytical querying of data collected over a period of time. e.g Calculate trends, summarize website logs but it can't be used for real time queries.
HBase fits for real-time querying of Big Data. Facebook use it for messaging and real-time analytics.
PIG can be used to construct dataflows, run a scheduled jobs, crunch big volumes of data, aggregate/summarize it and store into relation database systems. Good for ad-hoc analysis.
Hive can be used for ad-hoc data analysis but it can't support all un-structured data formats unlike PIG.
Consider that you work with RDBMS and have to select what to use - full table scans, or index access - but only one of them.
If you select full table scan - use hive. If index access - HBase.
Understanding in depth
Hadoop
Hadoop is an open source project of the Apache foundation. It is a framework written in Java, originally developed by Doug Cutting in 2005. It was created to support distribution for Nutch, the text search engine. Hadoop uses Google's Map Reduce and Google File System Technologies as its foundation.
Features of Hadoop
It is optimized to handle massive quantities of structured, semi-structured and unstructured data using commodity hardware.
It has shared nothing architecture.
It replicates its data into multiple computers so that if one goes down, the data can still be processed from another machine that stores its replica.
Hadoop is for high throughput rather than low latency. It is a batch operation handling massive quantities of data; therefore the response time is not immediate.
It complements Online Transaction Processing and Online Analytical Processing. However, it is not a replacement for a RDBMS.
It is not good when work cannot be parallelized or when there are dependencies within the data.
It is not good for processing small files. It works best with huge data files and data sets.
Versions of Hadoop
There are two versions of Hadoop available :
Hadoop 1.0
Hadoop 2.0
Hadoop 1.0
It has two main parts :
1. Data Storage Framework
It is a general-purpose file system called Hadoop Distributed File System (HDFS).
HDFS is schema-less
It simply stores data files and these data files can be in just about any format.
The idea is to store files as close to their original form as possible.
This in turn provides the business units and the organization the much needed flexibility and agility without being overly worried by what it can implement.
2. Data Processing Framework
This is a simple functional programming model initially popularized by Google as MapReduce.
It essentially uses two functions: MAP and REDUCE to process data.
The "Mappers" take in a set of key-value pairs and generate intermediate data (which is another list of key-value pairs).
The "Reducers" then act on this input to produce the output data.
The two functions seemingly work in isolation with one another, thus enabling the processing to be highly distributed in highly parallel, fault-tolerance and scalable way.
Limitations of Hadoop 1.0
The first limitation was the requirement of MapReduce programming expertise.
It supported only batch processing which although is suitable for tasks such as log analysis, large scale data mining projects but pretty much unsuitable for other kinds of projects.
One major limitation was that Hadoop 1.0 was tightly computationally coupled with MapReduce, which meant that the established data management vendors where left with two opinions:
Either rewrite their functionality in MapReduce so that it could be
executed in Hadoop or
Extract data from HDFS or process it outside of Hadoop.
None of the options were viable as it led to process inefficiencies caused by data being moved in and out of the Hadoop cluster.
Hadoop 2.0
In Hadoop 2.0, HDFS continues to be data storage framework.
However, a new and seperate resource management framework called Yet Another Resource Negotiater (YARN) has been added.
Any application capable of dividing itself into parallel tasks is supported by YARN.
YARN coordinates the allocation of subtasks of the submitted application, thereby further enhancing the flexibility, scalability and efficiency of applications.
It works by having an Application Master in place of Job Tracker, running applications on resources governed by new Node Manager.
ApplicationMaster is able to run any application and not just MapReduce.
This means it does not only support batch processing but also real-time processing. MapReduce is no longer the only data processing option.
Advantages of Hadoop
It stores data in its native from. There is no structure imposed while keying in data or storing data. HDFS is schema less. It is only later when the data needs to be processed that the structure is imposed on the raw data.
It is scalable. Hadoop can store and distribute very large datasets across hundreds of inexpensive servers that operate in parallel.
It is resilient to failure. Hadoop is fault tolerance. It practices replication of data diligently which means whenever data is sent to any node, the same data also gets replicated to other nodes in the cluster, thereby ensuring that in event of node failure,there will always be another copy of data available for use.
It is flexible. One of the key advantages of Hadoop is that it can work with any kind of data: structured, unstructured or semi-structured. Also, the processing is extremely fast in Hadoop owing to the "move code to data" paradigm.
Hadoop Ecosystem
Following are the components of Hadoop ecosystem:
HDFS: Hadoop Distributed File System. It simply stores data files as close to the original form as possible.
HBase: It is Hadoop's database and compares well with an RDBMS. It supports structured data storage for large tables.
Hive: It enables analysis of large datasets using a language very similar to standard ANSI SQL, which implies that anyone familier with SQL should be able to access data on a Hadoop cluster.
Pig: It is an easy to understand data flow language. It helps with analysis of large datasets which is quite the order with Hadoop. Pig scripts are automatically converted to MapReduce jobs by the Pig interpreter.
ZooKeeper: It is a coordination service for distributed applications.
Oozie: It is a workflow schedular system to manage Apache Hadoop jobs.
Mahout: It is a scalable machine learning and data mining library.
Chukwa: It is data collection system for managing large distributed system.
Sqoop: It is used to transfer bulk data between Hadoop and structured data stores such as relational databases.
Ambari: It is a web based tool for provisioning, managing and monitoring Hadoop clusters.
Hive
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data and makes querying and analyzing easy.
Hive is not
A relational database
A design for Online Transaction Processing (OLTP).
A language for real-time queries and row-level updates.
Features of Hive
It stores schema in database and processed data into HDFS.
It is designed for OLAP.
It provides SQL type language for querying called HiveQL or HQL.
It is familier, fast, scalable and extensible.
Hive Architecture
The following components are contained in Hive Architecture:
User Interface: Hive is a data warehouse infrastructure that can create interaction between user and HDFS. The User Interfaces that Hive supports are Hive Web UI, Hive Command line and Hive HD Insight(In Windows Server).
MetaStore: Hive chooses respective database servers to store the schema or Metadata of tables, databases, columns in a table, their data types and HDFS mapping.
HiveQL Process Engine: HiveQL is similar to SQL for querying on schema info on the Metastore. It is one of the replacements of traditional approach for MapReduce program. Instead of writing MapReduce in Java, we can write a query for MapReduce and process it.
Exceution Engine: The conjunction part of HiveQL process engine and MapReduce is the Hive Execution Engine. Execution engine processes the query and generates results as same as MapReduce results. It uses the flavor of MapReduce.
HDFS or HBase: Hadoop Distributed File System or HBase are the data storage techniques to store data into file system.
For a Comparison Between Hadoop Vs Cassandra/HBase read this post.
Basically HBase enables really fast read and writes with scalability. How fast and scalable? Facebook uses it to manage its user statuses, photos, chat messages etc. HBase is so fast sometimes stacks have been developed by Facebook to use HBase as the data store for Hive itself.
Where As Hive is more like a Data Warehousing solution. You can use a syntax similar to SQL to query Hive contents which results in a Map Reduce job. Not ideal for fast, transactional systems.
I worked on Lambda architecture processing Real time and Batch loads.
Real time processing is needed where fast decisions need to be taken in case of Fire alarm send by sensor or fraud detection in case of banking transactions.
Batch processing is needed to summarize data which can be feed into BI systems.
we used Hadoop ecosystem technologies for above applications.
Real Time Processing
Apache Storm: Stream Data processing, Rule application
HBase: Datastore for serving Realtime dashboard
Batch Processing
Hadoop: Crunching huge chunk of data. 360 degrees overview or adding context to events. Interfaces or frameworks like Pig, MR, Spark, Hive, Shark help in computing. This layer needs scheduler for which Oozie is good option.
Event Handling layer
Apache Kafka was first layer to consume high velocity events from sensor.
Kafka serves both Real Time and Batch analytics data flow through Linkedin connectors.
First of all we should get clear that Hadoop was created as a faster alternative to RDBMS. To process large amount of data at a very fast rate which earlier took a lot of time in RDBMS.
Now one should know the two terms :
Structured Data : This is the data that we used in traditional RDBMS and is divided into well defined structures.
Unstructured Data : This is important to understand, about 80% of the world data is unstructured or semi structured. These are the data which are on its raw form and cannot be processed using RDMS. Example : facebook, twitter data. (http://www.dummies.com/how-to/content/unstructured-data-in-a-big-data-environment.html).
So, large amount of data was being generated in the last few years and the data was mostly unstructured, that gave birth to HADOOP. It was mainly used for very large amount of data that takes unfeasible amount of time using RDBMS. It had many drawbacks, that it could not be used for comparatively small data in real time but they have managed to remove its drawbacks in the newer version.
Before going further I would like to tell that a new Big Data tool is created when they see a fault on the previous tools. So, whichever tool you will see that is created has been done to overcome the problem of the previous tools.
Hadoop can be simply said as two things : Mapreduce and HDFS. Mapreduce is where the processing takes place and HDFS is the DataBase where data is stored. This structure followed WORM principal i.e. write once read multiple times. So, once we have stored data in HDFS, we cannot make changes. This led to the creation of HBASE, a NOSQL product where we can make changes in the data also after writing it once.
But with time we saw that Hadoop had many faults and for that we created different environment over the Hadoop structure. PIG and HIVE are two popular examples.
HIVE was created for people with SQL background. The queries written is similar to SQL named as HIVEQL. HIVE was developed to process completely structured data. It is not used for ustructured data.
PIG on the other hand has its own query language i.e. PIG LATIN. It can be used for both structured as well as unstructured data.
Moving to the difference as when to use HIVE and when to use PIG, I don't think anyone other than the architect of PIG could say. Follow the link :
https://developer.yahoo.com/blogs/hadoop/comparing-pig-latin-sql-constructing-data-processing-pipelines-444.html
Let me try to answer in few words.
Hadoop is an eco-system which comprises of all other tools. So, you can't compare Hadoop but you can compare MapReduce.
Here are my few cents:
Hive: If your need is very SQLish meaning your problem statement can be catered by SQL, then the easiest thing to do would be to use Hive. The other case, when you would use hive is when you want a server to have certain structure of data.
Pig: If you are comfortable with Pig Latin and you need is more of the data pipelines. Also, your data lacks structure. In those cases, you could use Pig. Honestly there is not much difference between Hive & Pig with respect to the use cases.
MapReduce: If your problem can not be solved by using SQL straight, you first should try to create UDF for Hive & Pig and then if the UDF is not solving the problem then getting it done via MapReduce makes sense.
Pig: it is better to handle files and cleaning data
example: removing null values,string handling,unnecessary values
Hive: for querying on cleaned data
1.We are using Hadoop for storing Large data (i.e.structure,Unstructure and Semistructure data ) in the form file format like txt,csv.
2.If We want columnar Updations in our data then we are using Hbase tool
3.In case of Hive , we are storing Big data which is in structured format
and in addition to that we are providing Analysis on that data.
4.Pig is tool which is using Pig latin language to analyze data which is in any format(structure,semistructure and unstructure).
Cleansing Data in Pig is very easy,a suitable approach would be cleansing data through pig and then processing data through hive and later uploading it to hdfs.
Use of Hive, Hbase and Pig w.r.t. my real time experience in different projects.
Hive is used mostly for:
Analytics purpose where you need to do analysis on history data
Generating business reports based on certain columns
Efficiently managing the data together with metadata information
Joining tables on certain columns which are frequently used by using bucketing concept
Efficient Storing and querying using partitioning concept
Not useful for transaction/row level operations like update, delete, etc.
Pig is mostly used for:
Frequent data analysis on huge data
Generating aggregated values/counts on huge data
Generating enterprise level key performance indicators very frequently
Hbase is mostly used:
For real time processing of data
For efficiently managing Complex and nested schema
For real time querying and faster result
For easy Scalability with columns
Useful for transaction/row level operations like update, delete, etc.
Short answer to this question is -
Hadoop - Is Framework which facilitates distributed file system and programming model which allow us to store humongous sized data and process data in distributed fashion very efficiently and with very less processing time compare to traditional approaches.
(HDFS - Hadoop Distributed File system)
(Map Reduce - Programming Model for distributed processing)
Hive - Is query language which allows to read/write data from Hadoop distributed file system in a very popular SQL like fashion. This made life easier for many non-programming background people as they don't have to write Map-Reduce program anymore except for very complex scenarios where Hive is not supported.
Hbase - Is Columnar NoSQL Database. Underlying storage layer for Hbase is again HDFS. Most important use case for this database is to be able to store billion's of rows with million's of columns. Low latency feature of Hbase helps faster and random access of record over distributed data, is very important feature to make it useful for complex projects like Recommender Engines. Also it's record level versioning capability allow user to store transactional data very efficiently (this solves the problem of updating records we have with HDFS and Hive)
Hope this is helpful to quickly understand the above 3 features.
I believe this thread hasn't done in particular justice to HBase and Pig in particular. While I believe Hadoop is the choice of the distributed, resilient file-system for big-data lake implementations, the choice between HBase and Hive is in particular well-segregated.
As in, a lot of use-cases have a particular requirement of SQL like or No-SQL like interfaces. With Phoenix on top of HBase, though SQL like capabilities is certainly achievable, however, the performance, third-party integrations, dashboard update are a kind of painful experiences. However, it's an excellent choice for databases requiring horizontal scaling.
Pig is in particular excellent for non-recursive batch like computations or ETL pipelining (somewhere, where it outperforms Spark by a comfortable distance). Also, it's high-level dataflow implementations is an excellent choice for batch querying and scripting. The choice between Pig and Hive is also pivoted on the need of the client or server-side scripting, required file formats, etc. Pig supports Avro file format which is not true in the case of Hive. The choice for 'procedural dataflow language' vs 'declarative data flow language' is also a strong argument for the choice between pig and hive.
Hadoop:
HDFS stands for Hadoop Distributed File System which uses Computational processing model Map-Reduce.
HBase:
HBase is Key-Value storage, good for reading and writing in near real time.
Hive:
Hive is used for data extraction from the HDFS using SQL-like syntax. Hive use HQL language.
Pig:
Pig is a data flow language for creating ETL. It's an scripting language.
Pig is mostly dead after Cloudera got rid of it in CDP. Also last release on Apache was 19 June, 2017: release 0.17.0 so basically no committers actively working anymore. Use Spark or Python way more powerful than Pig.
I am considering various technologies for data warehousing and business intelligence, and have come upon this radical tool called Hadoop. Hadoop doesn't seem to be exactly built for BI purposes, but there are references of it having potential in this field. ( http://www.infoworld.com/d/data-explosion/hadoop-pitched-business-intelligence-488).
However little information I have got from the internet, my gut tells me that hadoop can become a disruptive technology in the space of traditional BI solutions. There really is sparse information regarding this topic, and hence I wanted to gather all the Guru's thoughts here on the potential of Hadoop as a BI tool as compared to traditional backend BI infrastructure like Oracle Exadata, vertica etc. For starters, I would like to ask the following question -
Design Considerations - How would designing a BI solution with Hadoop be different from traditional tools? I know it should be different, as I read one cannot create schemas in Hadoop. I also read that a major advantage will be the complete elimination of ETL tools for Hadoop (is this true?) Do we need Hadoop + pig + mahout to get a BI solution??
Thanks & Regards!
Edit - Breaking down into multiple questions. Will start with the one i think most imp.
Hadoop is a great tool to be part of a BI solution. It is not, itself, a BI solution. What Hadoop does is takes in Data_A and outputs Data_B. Whatever is needed for Bi but is not in a useful form can be processed using MapReduce and output a useful form of the data. Be it CSV, HIVE, HBase, MSSQL or anything else used to view data.
I believe Hadoop is supposed to be the ETL tool. That's what we are using it for. We process gigs of log files every hour and store it in Hive and do daily aggregations that are loading into a MSSQL server and viewed through a visualization layer.
The major design considerations I've run against are:
- Data Flexibility: Do you want your users to view pre-aggregated data or have the flexibility to adjust the query and look at the data how they want
- Speed: How long do you want your users to wait for the data? Hive (for example) is slow. It takes minutes to generate results, even on fairly small data sets. The larger the data traversed the longer it will take to generate a result.
- Visualization: What type of visualization do you want to use? Do you want to custom build a lot of pieces or be able to use something off the shelf? What restraints and flexibility are needed for your visualization? How flexible and changeable does the visualization need to be?
hth
Update: As a response to #Bhat's comment asking about lack of visualization...
The lack of a visualization tool that would allow us to effectively utilize the data stored in HBase was a major factor in re-evaluating our solution. We stored the raw data in Hive, and pre-aggregated the data and stored it HBase. To utilize this we were going to have to write a custom connector (did this part) and visualization layer. We looked at what we would be able to produce and what is commercially available, and went the commercial route.
We still use Hadoop as our ETL tool for processing our weblogs, it's fantastic for that. We just send the ETL'd raw data to a commercial big data database that will take the place of both Hive and HBase in our design.
Hadoop doesn't really compare to MSSQL or other data warehouse storage. Hadoop doesn't do any storage (ignoring the HDFS), it does processing of data. Running MapReduces (which Hive does) is going to be slower than MSSQL (or such).
Hadoop is very well suited for storing colossal files that can represent fact tables. These tables can be partitioned by placing individual files representing the table into separate directories. Hive understands such file structures and allows to query them like partitioned tables. You can phrase your BI questions to the Hadoop data in the form of SQL queries via Hive, but you will still need to write and run an occasional MapReduce job.
From business perspective, you should consider Hadoop if you have a lot of low-value data. There are many cases when RDBMS / MPP solutions are not cost effective.
You also should consider Hadoop as a serious option if your data is not structured (HTMLs for example).
We are creating a comparison matrix for BI tools for Big Data / Hadoop
http://hadoopilluminated.com/hadoop_book/BI_Tools_For_Hadoop.html
It is work in progress and would love any input.
(disclaimer : I am the author of this online book)
I'm interested in finding out how the recently-released (http://mirror.facebook.com/facebook/hive/hadoop-0.17/) Hive compares to HBase in terms of performance. The SQL-like interface used by Hive is very much preferable to the HBase API we have implemented.
It's hard to find much about Hive, but I found this snippet on the Hive site that leans heavily in favor of HBase (bold added):
Hive is based on Hadoop which is a batch processing system. Accordingly, this system does not and cannot promise low latencies on queries. The paradigm here is strictly of submitting jobs and being notified when the jobs are completed as opposed to real time queries. As a result it should not be compared with systems like Oracle where analysis is done on a significantly smaller amount of data but the analysis proceeds much more iteratively with the response times between iterations being less than a few minutes. For Hive queries response times for even the smallest jobs can be of the order of 5-10 minutes and for larger jobs this may even run into hours.
Since HBase and HyperTable are all about performance (being modeled on Google's BigTable), they sound like they would certainly be much faster than Hive, at the cost of functionality and a higher learning curve (e.g., they don't have joins or the SQL-like syntax).
From one perspective, Hive consists of five main components: a SQL-like grammar and parser, a query planner, a query execution engine, a metadata repository, and a columnar storage layout. Its primary focus is data warehouse-style analytical workloads, so low latency retrieval of values by key is not necessary.
HBase has its own metadata repository and columnar storage layout. It is possible to author HiveQL queries over HBase tables, allowing HBase to take advantage of Hive's grammar and parser, query planner, and query execution engine. See http://wiki.apache.org/hadoop/Hive/HBaseIntegration for more details.
Hive is an analytics tool. Just like pig, it was designed for ad hoc batch processing of potentially enourmous amounts of data by leveraging map reduce. Think terrabytes. Imagine trying to do that in a relational database...
HBase is a column based key value store based on BigTable. You can't do queries per se, though you can run map reduce jobs over HBase. It's primary use case is fetching rows by key, or scanning ranges of rows. A major feature is being able to have data locality when scanning across ranges of row keys for a 'family' of columns.
To my humble knowledge, Hive is more comparable to Pig. Hive is SQL-like and Pig is script based.
Hive seems to be more complicated with query optimization and execution engines as well as requires end user needs to specify schema parameters(partition etc).
Both are intend to process text files, or sequenceFiles.
HBase is for key value data store and retrieve...you can scan or filter on those key value pairs(rows). You can not do queries on (key,value) rows.
Hive and HBase are used for different purpose.
Hive:
Pros:
Apache Hive is a data warehouse infrastructure built on top of Hadoop.
It allows for querying data stored on HDFS for analysis via HQL, an SQL-like language, which will be converted into series of Map Reduce Jobs
It only runs batch processes on Hadoop.
it’s JDBC compliant, it also integrates with existing SQL based tools
Hive supports partitions
It supports analytical querying of data collected over a period of time
Cons:
It does not currently support update statements
It should be provided with a predefined schema to map files and directories into columns
HBase:
Pros:
A scalable, distributed database that supports structured data storage for large tables
It provides random, real time read/write access to your Big Data. HBase operations run in real-time on its database rather than MapReduce jobs
it supports partitions to tables, and tables are further split into column families
Scales horizontally with huge amount of data by using Hadoop
Provides key based access to data when storing or retrieving. It supports add or update rows.
Supports versoning of data.
Cons:
HBase queries are written in a custom language that needs to be learned
HBase isn’t fully ACID compliant
It can't be used with complicated access patterns (such as joins)
It is also not a complete substitute for HDFS when doing large batch MapReduce
Summary:
Hive can be used for analytical queries while HBase for real-time querying. Data can even be read and written from Hive to HBase and back again.
As of the most recent Hive releases, a lot has changed that requires a small update as Hive and HBase are now integrated. What this means is that Hive can be used as a query layer to an HBase datastore. Now if people are looking for alternative HBase interfaces, Pig also offers a really nice way of loading and storing HBase data. Additionally, it looks like Cloudera Impala may offer substantial performance Hive based queries on top of HBase. They are claim up to 45x faster queries over traditional Hive setups.
To compare Hive with Hbase, I'd like to recall the definition below:
A database designed to handle transactions isn’t designed to handle
analytics. It isn’t structured to do analytics well. A data warehouse,
on the other hand, is structured to make analytics fast and easy.
Hive is a data warehouse infrastructure built on top of Hadoop which is suitable for long running ETL jobs.
Hbase is a database designed to handle real time transactions