PS: Correct me if I am wrong in any line
I am building a search engine with Nutch and Solr.
I know by using Solr, I can enhance the efficiency of Searching- let Nutch do the crawling alone of the entire web.
I also know that Hadoop is used to handle petabytes of data by forming clusters and MapReduce.
Now , What i want to know is that
1) Since,I'll be running these open source softwares on only 1 machine,ie, my laptop on localhost... How would Hadoop be beneficial in my case as it forms clusters? How would clusters be formed on only 1 machine??
2) What would be the importance of MapReduce in my case?
3) How would MAHOUT,CASSANDRA and HBASE effect my engine???
Any help on this aspect is very much appreciated.Apologize me if I asked a noob question!!
Thanks
Regards
1) Since,I'll be running these open source software on only 1 machine,ie, my laptop on localhost... How would Hadoop be beneficial in my case as it forms clusters?
Hadoop was created to process large scale data. Hadoop is a
distributed application. It is not going to provide you benefits on a
single machine.
How would clusters be formed on only 1 machine??
Install Hadoop in pseudo cluster mode
What would be the importance of MapReduce in my case?
Again, if you want to process pages fetched by a crawler on the scale of 1000s of gigabyte. Map-Reduce is useful in processing such large data
How would MAHOUT,CASSANDRA and HBASE effect my engine???
They are different tools for different needs.
Mahout is machine
learning algorithms adapted for running as map-reduce tasks on Hadoop
or local files. Do you want to learn languages like Google Translate,
you can use it.
HBase is a no-sql database that provides more real time data
processing over ad hoc analysis for which map-reduce is more useful.
I would suggest that you go back to your problem statement, design with as little tools as required and when you hit the notes, you will understand when some of these tools could be useful.
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Let us consider the following problem. We have a system containing a huge amount of data (Big-Data). So, in fact we have a data base. As the first requirement we want to be able to write to and to read from the data base quickly. We also want to have a web-interface to the data-bases (so that different clients can write to and read from the data base remotely).
But the system that we want to have should be more than a data base. First, we want to be able to run different data-analysis algorithm on the data to find regularities, correlations, abnormalities and so on (as before we do care a lot about the performance). Second, we want to bind a machine learning machinery to the data-base. Which means that we want to run machine learning algorithms on the data to be able to learn "relations" present on the data and based on that predict the values of entries that are not yet in the data base.
Finally, we want to have a nice clicks based interface that visualize the data. So that the users can see the data in form of nice graphics, graphs and other interactive visualisation objects.
What are the standard and widely recognised approaches to the above described problem. What programming languages have to be used to deal with the described problems?
I will approach your question like this: I assume you are firmly interested in big data database use already and have a real need for one, so instead of repeating textbooks upon textbooks of information about them, I will highlight some that meet your 5 requirements - mainly Cassandra and Hadoop.
1) The first requirement we want to be able to write to and to read from the database quickly.
You'll want to explore NoSQL databases which are often used for storing “unstructured” Big Data. Some open-source databases include Hadoop and Cassandra. Regarding the Cassandra,
Facebook needed something fast and cheap to handle the billions of status updates, so it started this project and eventually moved it to Apache where it's found plenty of support in many communities (ref).
References:
Big Data and NoSQL: Five Key Insights
NoSQL standouts: New databases for new applications
Big data woes: Which database should I use?
Cassandra and Spark: A match made in big data heaven
List of NoSQL databases (currently 150)
2) We also want to have a web interface to the database
See the list of 150 NoSQL databases to see all the various interfaces available, including web interfaces.
Cassandra has a cluster admin, a web-based environment, a web-admin based on AngularJS, and even GUI clients.
References:
150 NoSQL databases
Cassandra Web
Cassandra Cluster Admin
3) We want to be able to run different data-analysis algorithm on the data
Cassandra, Hive, and Hadoop are well-suited for data analytics. For example, eBay uses Cassandra for managing time-series data.
References:
Cassandra, Hive, and Hadoop: How We Picked Our Analytics Stack
Cassandra at eBay - Cassandra Summit
An Introduction to Real-Time Analytics with Cassandra and Hadoop
4) We want to run machine learning algorithms on the data to be able to learn "relations"
Again, Cassandra and Hadoop are well-suited. Regarding Apache Spark + Cassandra,
Spark was developed in 2009 at UC Berkeley AMPLab, open sourced in
2010, and became a top-level Apache project in February, 2014. It has
since become one of the largest open source communities in big data, with over 200 contributors in 50+ organizations (ref).
Regarding Hadoop,
With the rapid adoption of Apache Hadoop, enterprises use machine learning as a key technology to extract tangible business value from their massive data assets.
References:
Getting Started with Apache Spark and Cassandra
What is Apache Mahout?
Data Science with Apache Hadoop: Predicting Airline Delays
5) Finally, we want to have a nice clicks-based interface that visualize the data.
Visualization tools (paid) that work with the above databases include Pentaho, JasperReports, and Datameer Analytics Solutions. Alternatively, there are several open-source interactive visualization tools such as D3 and Dygraphs (for big data sets).
References:
Data Science Central - Resources
Big Data Visualization
Start looking at:
what kind of data you want to store in the Database?
what kind of relationship between data you got?
how this data will be accessed? (for instance you need to access a certain set of data quite often)
are they documents? text? something else?
Once you got an answer for all those questions, you can start looking at which NoSQL Database you could use that would give you the best results for your needs.
You can choose between 4 different types: Key-Value, Document, Column family stores, and graph databases.
Which one will be the best fit can be determined answering the question above.
There are ready to use stack that may really help to start with your project:
Elasticsearch that would be your Database (it has a REST API that you can use to write them to the DB and to make queries and analysis)
Kibana is a visualization tool, it will allows you to explore and visualize your data, it it quite powerful and will be more than enough for most of your needs
Logstash can centralize the data processing and help you process and save them in elasticsearch, it already support quite few sources of logs and events, and you can also write your own plugin as well.
Some people refers to them as the ELK stack.
I don't believe you should worry about the programming language you have to use at this point, try to select the tools first, sometimes the choices are limited by the tools you want to use and you can still use a mixture of languages and make the effort only if/when it make sense.
A common way to solve such a requirements is to use Amazon Redshift and the ecosystem around it.
Redshift is a peta-scale data warehouse (it can also start with giga-scale), that exposes Ansi SQL interface. As you can put as much data as you like into the DWH and you can run any type of SQL you wish against this data, this is a good infrastructure to build almost any agile and big data analytics system.
Redshift has many analytics functions, mainly using Window functions. You can calculate averages and medians, but also percentiles, dense rank etc.
You can connect almost every SQL client you want using JDBS/ODBC drivers. It can be from R, R studio, psql, but also from MS-Excel.
AWS added lately a new service for Machine Learning. Amazon ML is integrating nicely with Redshift. You can build predictive models based on data from Redshift, by simply giving an SQL query that is pulling the data needed to train the model, and Amazon ML will build a model that you can use both for batch prediction as well as for real-time predictions. You can check this blog post from AWS big data blog that shows such a scenario: http://blogs.aws.amazon.com/bigdata/post/TxGVITXN9DT5V6/Building-a-Binary-Classification-Model-with-Amazon-Machine-Learning-and-Amazon-R
Regarding visualization, there are plenty of great visualization tools that you can connect to Redshift. The most common ones are Tableau, QliView, Looker or YellowFin, especially if you don't have any existing DWH, where you might want to keep on using tools like JasperSoft or Oracle BI. Here is a link to a list of such partners that are providing free trial for their visualization on top of Redshift: http://aws.amazon.com/redshift/partners/
BTW, Redshift also provides a free trial for 2 months that you can quickly test and see if it fits your needs: http://aws.amazon.com/redshift/free-trial/
Big Data is a tough problem primarily because it isn't one single problem. First if your original database is a normal OLTP database that is handling business transactions throughout the day, you will not want to also do your big data analysis on this system since the data-analysis you will want to do will interfere with the normal business traffic.
Problem #1 is what type of database do you want to use for data-analysis? You have many choices ranging from RDBMS, Hadoop, MongoDB, and Spark. If you go with RDBMS then you will want to change the schema to be more compliant with data-analysis. You will want to create a data warehouse with a star schema. Doing this will make many tools available to you because this method of data analysis has been around for a very long time. All of the other "big data" and data analysis databases do not have the same level of tooling available, but they are quickly catching up. Each one of these will require research on which one you will want to use based on your problem set. If you have big batches of data RDBMS and Hadoop will be good. If you have streaming types of data then you will want to look at MongoDB and Spark. If you are a Java shop then RDBMS, Hadoop or Spark. If you are JavaScript MongoDB. If you are good with Scala then Spark.
Problem #2 is getting your data from your transactional database into your big data storage. You will need to find a programming language that has libraries to talk to both databases and you will have to decide when and where you will be moving this data. You can use Python, Java or Ruby to do this work.
Problem #3 is your UI. If you decide to go with RDBMS then you can use many of the available tools available or you can build your own. The other data storage solutions will have tool support but it isn't as mature is that available for the RDBMS. You are most likely going to build your own here anyway because your analysts will want to have the tools built to their specifications. Java works with all of these storage mechanisms but you can probably get Python to work too. You may want to provide a service layer built in Java that provides a RESTful interface and then put a web layer in front of that service layer. If you do this, then your web layer can be built in any language you prefer.
These three languages are most commonly used for machine learning and data mining on the Server side: R, Python, SQL. If you are aiming for heavy mathematical functions and graph generation, Haskell is very popular.
I am currently working on a long term project that will need to support:
Lots of fast Read/Write operations via RESTful Services
An Analytics Engine continually reading and making sense of data
It is vital that the performance of the Analytics Engine not be affected by the volume of Reads/Writes coming from the API calls.
Because of that, I'm thinking that I may have to use a "front-end" database and some sort of "back-end" data warehouse. I would also need to have something like Elastic Search or Solr indexing the data stored in the data warehouse.
The Questions:
Is this a Recommended Setup? What would the alternative be?
If so...
I'm considering either Hive or Pig for the data-warehousing, and Elastic Search or Solr as a Search Engine. Which combination is known to work better together?
And finally...
I'm seriously considering Cassandra as the "fron-end" database. What is the relation between Cassandra and Hadoop, and when/why should they be put to work together instead of having just Cassandra?
Please note, my intention is NOT to start a debate about which of these is better, but to understand how can they be put to work better more efficiently. If it makes any difference, the main code is being written in Scala and Java.
I truly appreciate your help. I'm basically learning as I go and all comments will be very helpful.
Thank you.
First let's talk about Cassandra
This is a NoSQL database with eventual consistency which basically means for you that different nodes into a Cassandra cluster may have different 'snapshots' of data in the case that there is an inter cluster communication/availability problem. The data eventually will be consistent however.
Since you consider it as a 'frontend' database what you need to understand is how you will model your data. Cassandra can take advantage of indexes however you still need to defined upfront your access pattern.
Normally there is no relation between Cassandra and Hadoop (except that both are written in Java) however the Datastax distribution (enterprise version) has Hadoop support directly from Cassandra.
As a general workflow you will read/write most current data (let's say - last 24 hours) from your 'small' database that enough performance (Cassandra has excellent support for it) and you would move anything older than X (older than 24 hours) to a 'long term storage' such as Hadoop where you can run all sort of Map Reduce etc.
In regards to the text search it really depends what you need - Elastic Search is sort of competition to Solr and reverse. You can see yourself how they compare here http://solr-vs-elasticsearch.com/
As for your third question,
I think Cassandra is more like a database to save data.
Hadoop is responsible to provide a compution model to let you analyze your large data in
Cassandra.
So it is very helpful to combine Cassandra with Hadoop.
Also have other ways you can consider, such as combine with mongo and hadoop,
for mongo has support mongo-connector between hadoop and it's data.
Also if you have some search requirements , you can also use solr, directly generated index from mongo.
PREFACE:
This question is not asking for an open ended comparison of Elastic Search vs. Solr vs. Datastax Solr (Datastax EE). (Though links in comments section for this are welcome).
PROJECT:
I have been building a domain name type web service for a while. In doing so, I am realizing the exponential growth of such service.
BACKGROUND:
I would like to know which specific search platform allows me to save and expand indefinitely. Yes, I realize you can split a Solr Shard these days– so if I have a 20 shard solr cloud I can later split them into 40 (I think? Again... that's not indefinate). Not sure on the Elastic Search side of things. Datastax (EE) seems to be the answer because of Cassandra’s architecture but (A) Since they give no transparency on license price – and I have to disclose my earnings to them I'm quickly reminded of Oracle's bleed you slowly fee strategy and as I start-up that is a huge deterrent. Also, (B) When they say they integrate full MapReduce with Hive, Sqop, Mahout, Solr, and Pig – I’m thinking I don’t want to spend a lifetime learning bells and whistles that aren’t applicable to my project. I want a search platform that I can add 2 billion documents a month (or whatever number) indefinitely and not have to worry that I started a cluster with too little shards upfront.
QUESTION:
Admittedly my background section is pilfered with ignorance that I would like to correct. My intention is not to offend or dilute these amazing technologies. I am simply wondering which of them can scale w/o having to worry about overgrowing shards [I took out the word forever here -- thank you per comment below]. Or can any? Not hardware-wise, but Shards. Which platform can I use and not have to worry about the future growth whether its 20TB or 2PB. Assume hardware budget for servers, switches, etc. etc. are indefinite.
DataStax Enterprise (DSE) is not a "search platform" per se. One of the features DSE provides is the ability to search data stored in Cassandra. Cassandra is being used to store and access enterprise operational data. The idea is that once you have decided that Cassandra is your preferred data store for your enterprise operational data, the DSE/Solr integration then allows you to perform rich search on that data.
Large enterprises are looking to migrate off of traditional relational databases, to more modern platforms such as NoSQL databases, such as Cassandra, where scalability and distributed computing (including multi-data center support, tunable consistency, and robust operations tools, including the OpsCenter GUI dashboard) are the norm. The Solr integration of DSE facilitates that migration.
With regards to your revenue, that link points to a startup program. That makes the software 100% free if you qualify.
I hope I can found a distributed filesystem which is easy to configure, easy to use, easy to learn.
Any one can help on this?
As the details relating the usage is not mentioned and as much i can infer from the question, you must try MogileFS (Easy in setting it up and maintaining). Its is from the maker's of memcached and is used to server images etc.
Please refer to the below mentioned link for better explanation.
http://code.google.com/p/mogilefs/
Lustre, Gluster or MogileFS?? for video storage, encoding and streaming
I suggest you consider of using Apache Hadoop. It has a lot of services and technologies to work with (Cassandra, HBase, etc). Quote from official site:
Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Basically, Hadoop is a large framework. You can use Karmasphere studio with Hadoop. I suppose, with its help you can learn Hadoop much quicker get deeper into distibuted systems.
About HDFS: read the article "GridGain and Hadoop". Short quote from there:
today HDFS is probably the most economical way to keep very large static data set of TB and PB scale in distributed file system for a long term storage
Check out Amazon Simple Storage Service (Amazon S3).
It has (practically) unlimited storage, 100% uptime and tick most of the boxes needed for most situations. It isn't free, but is very cheap considering what you get.
For a new application based on Erlang, Python, we are thinking of trying out a non-RDBMS database(just for the sake of it). Some of the databases I've researched are Mongodb, CouchDB, Cassandra, Redis, Riak, Scalaris). Here is a list of simple requirements.
Ease of development - I need to make a quick proof-of-concept demo. So the database needs to have good adapters for Eralang and Python.
I'm working on a new application where we have lots of "connected" data. Somebody recommended Neo4j for graph-like data. Any ideas on that?
Scalable - We are looking at a distributed architecture, hence scalability is important.
For the moment performance(in any form) isn't exactly on top of my list, and I don't think we'll be hitting the limitations of any of the above mentioned databases anytime soon.
I'm just looking for a starting point for non-RDBMS database. Any recommendations?
We have used Mnesia in building an Enterprise Application. Mnesia when in a mode where the tables are Fragmented performs at its best because it would not have table size limits. Mnesia has performed well for the last 1 year and is still on. We have around 15 million records per table on the average and around 24 tables in a given database Schema.
I recommend mnesia Database especially the one that comes shipped within Erlang 14B03 at the Erlang.org website. We have used CouchDB and Membase Server (http://www.couchbase.com)for some parts of the system but mnesia is the main data storage (primary storage). Backups have been automated very well and the system scales well against increasing size of data yet tables running under many checkpoints. Its distribution, auto-replication and Complex Data Model enabled us to build the application very quickly without worrying about replication, scalability and fail-over / take-over of systems.
Mnesia Scales well and it's schema can be configured and changes while the database is running. Tables can be moved, copied, altered e.t.c while the system is live. Generally, it has all features of powerful systems built on top of Erlang/OTP. When you google mnesia DBMS, you will get a number of books and papers that will tell you more.
Most importantly, our application is Web based, powered by Yaws web server (yaws.hyber.org) and we are impressed with Mnesia's performance. Its record look up speeds are very good and the system feels so light yet renders alot of data. Do give mnesia a try and you will not regret it.
EDIT: To quickly use it in your application, look at the answer given here
Ease of development - I need to make a quick proof-of-concept demo. So the database needs to have good adapters for Eralang and Python.
Riak is written in Erlang => speaks Erlang natively
I'm working on a new application where we have lots of "connected" data. Somebody recommended Neo4j for graph-like data. Any ideas on that?
Neo4j is great for "connected" data. It has Python bindings, and some Erlang adapters How to Use Neo4j From Erlang. Thing to note, Neo4j is not as easy to Scale Out, at least for free. But.. it is fully transactional ( even JTA ), it persists things to disk, it is baked into Spring Data.
Scalable - We are looking at a distributed architecture, hence scalability is important.
For the moment performance(in any form) isn't exactly on top of my list, and I don't think we'll be hitting the limitations of any of the above mentioned databases anytime soon.
I believe given your input, Riak would be the best choice for you:
Written in Erlang
Naturally Distributed
Very easy to develop for/with
Lots of features ( secondary indicies, virtual nodes, fully modular, pluggable persistence [LevelDB, Bitcask, InnoDB, flat file, etc.. ], extremely reliable, built in full text search, etc.. )
Has an extremely passionate and helpful community with Basho backing it up