I want to know following things so that I can fix my server architecture and make it more flexible.
Is it good to store home feed data [ex: Facebook homeFeed] to the variable for future manipulation or just fetch data related to homeFeed and manipulate everything which needs to be done on run time.
Please note that data set of home feed can contain anything. [ not developed yet ]
Is there any limit to request to MongoDB at any given time which can create a delay in data processing?
Are node.js and MongoDB a good option for social network development?
If you know anything related to social network development then please share the pros and cons.
Is it good to store home feed data [ex: Facebook homeFeed] to the variable for future manipulation or just fetch data related to homeFeed and manipulate everything which needs to be done on run time.
You can (and sometimes should) pre-compute home feed data for certain users (for example those who are the most active). You don't store that in a variable though, you cache the results with something like Redis.
Generating the home feed on a "request" basis is also possible and good.
Both approaches require careful thinking about your system's architecture, performance, scalability, robustness, fault-tolerance, etc...
Is there any limit to request to MongoDB at any given time. which can create a delay in data processing?
Yes. A MongoDB instance (or any other database) has limited resources. Look at the Sharding and Replication docs of MongoDB for more info about how to work with MongoDb at scale.
Are node.js and MongoDB are a good option for social network development.
Node.js and MongoDb are a good combinations for quick prototyping, you can get productive fairly quickly. Any language(s) you are familiair with is/are a good choice here, since your focus seems to be on architecture. Go, Java and PHP are good candidates too.
In the real world social networks are built with a lot more tools than that. Since the teams use various programming languages, databases and frameworks depending on the task at hand.
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What are the core architectural differences between these technologies?
Also, what use cases are generally more appropriate for each?
Update
Now that the question scope has been corrected, I might add something in this regard as well:
There are many comparisons between Apache Solr and ElasticSearch available, so I'll reference those I found most useful myself, i.e. covering the most important aspects:
Bob Yoplait already linked kimchy's answer to ElasticSearch, Sphinx, Lucene, Solr, Xapian. Which fits for which usage?, which summarizes the reasons why he went ahead and created ElasticSearch, which in his opinion provides a much superior distributed model and ease of use in comparison to Solr.
Ryan Sonnek's Realtime Search: Solr vs Elasticsearch provides an insightful analysis/comparison and explains why he switched from Solr to ElasticSeach, despite being a happy Solr user already - he summarizes this as follows:
Solr may be the weapon of choice when building standard search
applications, but Elasticsearch takes it to the next level with an
architecture for creating modern realtime search applications.
Percolation is an exciting and innovative feature that singlehandedly
blows Solr right out of the water. Elasticsearch is scalable, speedy
and a dream to integrate with. Adios Solr, it was nice knowing you. [emphasis mine]
The Wikipedia article on ElasticSearch quotes a comparison from the reputed German iX magazine, listing advantages and disadvantages, which pretty much summarize what has been said above already:
Advantages:
ElasticSearch is distributed. No separate project required. Replicas are near real-time too, which is called "Push replication".
ElasticSearch fully supports the near real-time search of Apache
Lucene.
Handling multitenancy is not a special configuration, where
with Solr a more advanced setup is necessary.
ElasticSearch introduces
the concept of the Gateway, which makes full backups easier.
Disadvantages:
Only one main developer [not applicable anymore according to the current elasticsearch GitHub organization, besides having a pretty active committer base in the first place]
No autowarming feature [not applicable anymore according to the new Index Warmup API]
Initial Answer
They are completely different technologies addressing completely different use cases, thus cannot be compared at all in any meaningful way:
Apache Solr - Apache Solr offers Lucene's capabilities in an easy to use, fast search server with additional features like faceting, scalability and much more
Amazon ElastiCache - Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory cache in the cloud.
Please note that Amazon ElastiCache is protocol-compliant with Memcached, a widely adopted memory object caching system, so code, applications, and popular tools that you use today with existing Memcached environments will work seamlessly with the service (see Memcached for details).
[emphasis mine]
Maybe this has been confused with the following two related technologies one way or another:
ElasticSearch - It is an Open Source (Apache 2), Distributed, RESTful, Search Engine built on top of Apache Lucene.
Amazon CloudSearch - Amazon CloudSearch is a fully-managed search service in the cloud that allows customers to easily integrate fast and highly scalable search functionality into their applications.
The Solr and ElasticSearch offerings sound strikingly similar at first sight, and both use the same backend search engine, namely Apache Lucene.
While Solr is older, quite versatile and mature and widely used accordingly, ElasticSearch has been developed specifically to address Solr shortcomings with scalability requirements in modern cloud environments, which are hard(er) to address with Solr.
As such it would probably be most useful to compare ElasticSearch with the recently introduced Amazon CloudSearch (see the introductory post Start Searching in One Hour for Less Than $100 / Month), because both claim to cover the same use cases in principle.
I see some of the above answers are now a bit out of date. From my perspective, and I work with both Solr(Cloud and non-Cloud) and ElasticSearch on a daily basis, here are some interesting differences:
Community: Solr has a bigger, more mature user, dev, and contributor community. ES has a smaller, but active community of users and a growing community of contributors
Maturity: Solr is more mature, but ES has grown rapidly and I consider it stable
Performance: hard to judge. I/we have not done direct performance benchmarks. A person at LinkedIn did compare Solr vs. ES vs. Sensei once, but the initial results should be ignored because they used non-expert setup for both Solr and ES.
Design: People love Solr. The Java API is somewhat verbose, but people like how it's put together. Solr code is unfortunately not always very pretty. Also, ES has sharding, real-time replication, document and routing built-in. While some of this exists in Solr, too, it feels a bit like an after-thought.
Support: there are companies providing tech and consulting support for both Solr and ElasticSearch. I think the only company that provides support for both is Sematext (disclosure: I'm Sematext founder)
Scalability: both can be scaled to very large clusters. ES is easier to scale than pre-Solr 4.0 version of Solr, but with Solr 4.0 that's no longer the case.
For more thorough coverage of Solr vs. ElasticSearch topic have a look at https://sematext.com/blog/solr-vs-elasticsearch-part-1-overview/ . This is the first post in the series of posts from Sematext doing direct and neutral Solr vs. ElasticSearch comparison. Disclosure: I work at Sematext.
I see that a lot of folks here have answered this ElasticSearch vs Solr question in terms of features and functionality but I don't see much discussion here (or elsewhere) regarding how they compare in terms of performance.
That is why I decided to conduct my own investigation. I took an already coded heterogenous data source micro-service that already used Solr for term search. I switched out Solr for ElasticSearch then I ran both versions on AWS with an already coded load test application and captured the performance metrics for subsequent analysis.
Here is what I found. ElasticSearch had 13% higher throughput when it came to indexing documents but Solr was ten times faster. When it came to querying for documents, Solr had five times more throughput and was five times faster than ElasticSearch.
Since the long history of Apache Solr, I think one strength of the Solr is its ecosystem. There are many Solr plugins for different types of data and purposes.
Search platform in the following layers from bottom to top:
Data
Purpose: Represent various data types and sources
Document building
Purpose: Build document information for indexing
Indexing and searching
Purpose: Build and query a document index
Logic enhancement
Purpose: Additional logic for processing search queries and results
Search platform service
Purpose: Add additional functionalities of search engine core to provide a service platform.
UI application
Purpose: End-user search interface or applications
Reference article : Enterprise search
I have been working on both solr and elastic search for .Net applications.
The major difference what i have faced is
Elastic search :
More code and less configuration, however there are api's to change
but still is a code change
for complex types, type within types i.e nested types(wasn't able to achieve in solr)
Solr :
less code and more configuration and hence less maintenance
for grouping results during querying(lots of work to achieve in
elastic search in short no straight way)
I have created a table of major differences between elasticsearch and Solr and splunk, you can use it as 2016 update:
While all of the above links have merit, and have benefited me greatly in the past, as a linguist "exposed" to various Lucene search engines for the last 15 years, I have to say that elastic-search development is very fast in Python. That being said, some of the code felt non-intuitive to me. So, I reached out to one component of the ELK stack, Kibana, from an open source perspective, and found that I could generate the somewhat cryptic code of elasticsearch very easily in Kibana. Also, I could pull Chrome Sense es queries into Kibana as well. If you use Kibana to evaluate es, it will further speed up your evaluation. What took hours to run on other platforms was up and running in JSON in Sense on top of elasticsearch (RESTful interface) in a few minutes at worst (largest data sets); in seconds at best. The documentation for elasticsearch, while 700+ pages, didn't answer questions I had that normally would be resolved in SOLR or other Lucene documentation, which obviously took more time to analyze. Also, you may want to take a look at Aggregates in elastic-search, which have taken Faceting to a new level.
Bigger picture: if you're doing data science, text analytics, or computational linguistics, elasticsearch has some ranking algorithms that seem to innovate well in the information retrieval area. If you're using any TF/IDF algorithms, Text Frequency/Inverse Document Frequency, elasticsearch extends this 1960's algorithm to a new level, even using BM25, Best Match 25, and other Relevancy Ranking algorithms. So, if you are scoring or ranking words, phrases or sentences, elasticsearch does this scoring on the fly, without the large overhead of other data analytics approaches that take hours--another elasticsearch time savings.
With es, combining some of the strengths of bucketing from aggregations with the real-time JSON data relevancy scoring and ranking, you could find a winning combination, depending on either your agile (stories) or architectural(use cases) approach.
Note: did see a similar discussion on aggregations above, but not on aggregations and relevancy scoring--my apology for any overlap.
Disclosure: I don't work for elastic and won't be able to benefit in the near future from their excellent work due to a different architecural path, unless I do some charity work with elasticsearch, which wouldn't be a bad idea
If you are already using SOLR, remain stick to it. If you are starting up, go for Elastic search.
Maximum major issues have been fixed in SOLR and it is quite mature.
Imagine the use case:
A lot(100+) of small(10Mb-100Mb, 1000-100000 documents) search indexes.
They are using by a lot of applications (microservices)
Each application can use more than one index
Small by size index, yes. But huge load(hundreds search-requests per second) and requests are complex (multiple aggregations, conditions and so on)
Downtimes are not allowed
All of that is working years long, and constantly growing.
Idea to have individual ES instance per each index - is huge overhead in this case.
Based on my experience, this kind of use case is very complex to support with Elasticsearch.
Why?
FIRST.
The major problem is fundamental back compatibility disregard.
Breaking changes are so cool!
(Note: imagine SQL-server which require you to do small change in all your SQL-statements, when upgraded... can't imagine it. But for ES it's normal)
Deprecations which will dropped in next major release are so sexy!
(Note: you know, Java contain some deprecations, which 20+ years old, but still working in actual Java version...)
And not only that, sometimes you even have something which nowhere documented (personally came across only once but... )
So. If you want to upgrade ES (because you need new features for some app or you want to get bug fixes) - you are in hell. Especially if it is about major version upgrade.
Client API will not back compatible. Index settings will not back compatible.
And upgrade all app/services same moment with ES upgrade is not realistic.
But you must do it time to time. No other way.
Existing indexes is automatically upgraded? - Yes. But it not help you when you will need to change some old-index settings.
To live with that, you need constantly invest a lot of power in ... forward compatibility of you apps/services with future releases of ES.
Or you need to build(and anyway constantly support) some kind of middleware between you app/services and ES, which provide you back compatible client API.
(And, you can't use Transport Client (because it required jar upgrade for every minor version ES upgrade), and this fact do not make your life easier)
Is it looks simple & cheap? No, it's not. Far from it.
Continuous maintenance of complex infrastructure which based on ES, is way to expensive in all possible senses.
SECOND.
Simple API ? Well... no really.
When you is really using complex conditions and aggregations.... JSON-request with 5 nested levels is whatever, but not simple.
Unfortunately, I have no experience with SOLR, can't say anything about it.
But Sphinxsearch is much better it this scenario, becasue of totally back compatible SphinxQL.
Note:
Sphinxsearch/Manticore are indeed interesting. It's not Lucine based, and as result seriously different. Contain several unique features from the box which ES do not have and crazy fast with small/middle size indexes.
I have use Elasticsearch for 3 years and Solr for about a month, I feel elasticsearch cluster is quite easy to install as compared to Solr installation. Elasticsearch has a pool of help documents with great explanation. One of the use case I was stuck up with Histogram Aggregation which was available in ES however not found in Solr.
Add an nested document in solr very complex and nested data search also very complex. but Elastic Search easy to add nested document and search
I only use Elastic-search. Since I found solr is very hard to start.
Elastic-search's features:
Easy to start, very few setting. Even a newbie can setup a cluster step by step.
Simple Restful API which using NoSQL query. And many language libraries for easy accessing.
Good document, you can read the book: . There is a web version on official website.
Ok, here's the thing.
I have a good JS background, had my share of JS in the past, and have lots of cool bare-bones tools I take with me from project to project that act like a library.
I'm trying to formulate work with CouchDB.
Now, after getting used to luxury of cool tools that you wrote and simplify the language for you - I find it a little frustrating to write many things in bare-bones manner.
I'm looking for a way I can load to the database context a limited, highly efficient and generic set of tools that focus on the pure language and makes the work with the language much more groovy (and gosh, no, im not talking about jquery or any of the even more busty libraries out there).
If on top of that, there could be found a way where I can add to the execution context of the couchDB JS engine some of my own logic tools (BL model functions) - it would present a great and admirable power and make couchDB the new home for a JavaScript-er like me.
Maybe I'm aiming too low.
I'd be satisfied with a way I can allocate a set of extensions even for a specific database, and I don't mind do it for every database in separate. Or worse - to add it to every design document, so I can teach for example several views in the same design-doc what a Person is, what a Worker is, and use their methods to retrieve data from them according to logic in a reusably coded manner.
Can anybody point me the the way?
Whatever way you can point me - I'll be very verrry grateful.
If there are ways for all of these - then great.
Trust me to know the difference of what logic belongs to what layer...
You open my possibilities - I promise to use them :D
CouchDB now supports code sharing as CommonJS modules.
http://docs.couchbase.org/couchdb-release-1.1/index.html#couchdb-release-1.1-commonjs
http://caolanmcmahon.com/posts/commonjs_modules_in_couchdb
In this way, you can share your javascript modules between views, lists, and shows in the same design doc. (Server-side)
Also, you can load these modules on the browser side with this library:
https://github.com/couchapp/couchapp/blob/master/couchapp/templates/vendor/couchapp/_attachments/jquery.couch.app.js
You also might want to look at Kanso:
http://kansojs.org/
It does a really good job of making your javascript work seemless between the server and client.
You can find some helpful tools here : https://github.com/vivekpathak/casters
The running examples and test cases may particularly help you.
As stated in the title, I would like to know if it's safe to develop a website using one of the actuals "omg" platforms that are Node.js and Ringo.js at their actual version.
Also, I would like to know if they support cookies/sessions and how do they deals with multi-fields post (fieldname[] in PHP).
Thank you
--Edit--
Thanks for all the links guys.
What can you tell me about Ringojs ?
Since I haven't figured which platform to start playing with. I must admit that the fact it can use Java seamlessly really impress me. The only available XSLT 2.0 library is in Java. I could use it as a templating system.
Is there anyone who had the chance to play with Ringojs?
From my experience using both, Ringo is more stable and "safer" for production use but you can comfortably deploy both. In addition to the ability to wrap existing Java libraries that you mention, you also get the benefit of being able to run it in an existing webapp container which manages the lifecycle of the application for you and ensures its availability.
That being said, it doesn't have to be an either or decision. By using my common-node package and assuming you don't use any Java libraries, it's perfectly feasible to maintain a project that runs on both without any changes to the code.
I've also included benchmarks that test the performance of Node.js vs. RingoJS the results of which you can find in the common-node/README.md. To summarize: RingoJS has slightly lower throughput than Node.js, but much lower variance in response times while using six times the RAM with default Java settings. The latter can be tweaked and brought down to as little as twice the memory usage of Node with e.g. my ringo-sunserver but at the expense of decreased performance.
Node.js is stable, so yes it's safe to use. Node.js is capable of handling cookies, sessions, and multiple fields but are not as easy to manage. Web frameworks solve this problem.
I recommend Express.js, it's an open-source web framework for Node.js which handles all of this and more.
You can download it here:
https://github.com/visionmedia/express
I hope this helped!
Examples of some of the bigger sites running Node.js
https://www.learnboost.com/
http://ge.tt/
https://gomockingbird.com/
https://secured.milewise.com/
http://voxer.com/
https://www.yammer.com/
http://cloud9ide.com/
http://beta.etherpad.org/
http://loggly.com/
http://wordsquared.com/
Yes. It is. https://github.com/joyent/node/wiki/Projects,-Applications,-and-Companies-Using-Node and https://github.com/joyent/node/wiki/modules
cookies/sessions/forms etc http://expressjs.com/ makes it easier
Ringojs is a framework developed by Hannes Wallnöver and uses rhino as it's scripting framework. There are webframeworks, templating-engines, orm-packages and many many more things already available. Have a look at the tutorial featuring a good subset of packages you may use for a simple web-application. It's not too long and straightforward.
Even thought some of those packages used within the tutorial (e.g. ringo-sqlstore]) are marked as 0.8 and come with the hint "consider this being beta" they are already very stable and bugs - if you find one - get fixed or commented on very fast.
And the power of uncountable java-libraries out there is at your fingertips - so if you already have java-knowledge this knowledge isn't wasted. Rhino - the scripting-engine - even enables you to implement interfaces and extend classes. It is possible a little more advanced but i've done it and i know of packages taking advantage of such features (like ringo-ftpserver which is a wrapper around Apache FtpServer written in java)
Another pro for me is - because ringojs is based on java - it works fairly well with multithreading with ringo/worker for example.
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I am looking for an eventually consistent data store and it looks like it may be coming down to Riak or Cassandra. Has anyone got expereinces of a view on this?
As you probably know, they are both architecturally strongly influenced by Dynamo (eventually consistent, no single points of failure, etc). Both also go beyond Dynamo in providing a "richer than pure K/V" data model -- in Cassandra's case, providing a Bigtable-like ColumnFamily mode, in Riak's, a Document-oriented one. I have seen sane people choose both.
I believe points that favor Cassandra include
speed
support for clusters spanning multiple data centers
big names using it (digg, twitter, facebook, webex, ... -- http://n2.nabble.com/Cassandra-users-survey-tp4040068p4040393.html)
Points that favor Riak include
map/reduce support out of the box
/Cassandra dev, fwiw
Riak is used by
Mozilla Foundation
Ask.com sponsored listings
Comcast
Citigroup
Bet365
I think they both pass the test of credible reference customers/users.
Cassandra seems more mature, and is currently doing better in benchmarks. Riak seems easier to add a node to as your cluster grows.
For completeness: A good (probably biased) comparison between the two can be found at http://docs.basho.com/riak/1.3.2/references/appendices/comparisons/Riak-Compared-to-Cassandra/
Use and download are different. Best to get references.
Perhaps a private conversation could be had where Riak references in these companies could be shared? Not sure how to get such with Cassandra, but there is a community of companies that support Cassandra that seem like a good place to start. As these probably have community participants in Cassandra development, it may be a REALLY reasonable place to start.
I would like to hear Riak's answer to recent and large deployments where customers are happy.
I also would like to see the roadmap for each product. Cassandra is a bit easier to track (http://wiki.apache.org/cassandra/) than Riak in my view as Cassandra's wiki discusses limitations and things that are probably going to change going forward, but neither outline futures well. I could understand that of an open source community ... perhaps ... but I cannot for a product for which I must pay.
I also would suggest research of Cloudant, which has what appears to be a very nice layering of capabilities. It also looks like it is bringing to bear the capabilities elsewhere in Apache land. CouchDB is the Apache platform on which Cloudant is based. BUT the indexing with Lucene seems but the tip of the iceberg when it comes to where Cloudant could go. Creating and managing an index is a very systematic process, a kind of data pipeline, that could be scripted using other Apache community assets. AND capabilities like NLP also could be added through Lucene indirectly, or maybe directly into what is persisted.
It would be nice to see a proposed Cloudant roadmap, especially since the team could mine the riches of the Apache community and integrate such into Cloudant. Such probably exists as there is an operational component to the Cloudant revenue model that will require it, if for no other reason.
Another area of interest ... Cloudant's pricing model ... it is clear their revenue model is not based on software, but around service. That is quite attractive, and it seems consistent with the ecosystem surrounding Cassandra too. I don't know if the Basho folks have won over enough of the nosql community as yet ... don't see such from any buzz around their web site or product.
I like this Cloudant web page (https://cloudant.com/the-data-layer/). I was surprised to see the embedded Erlang capability ... I did not know CouchDB was written in Erlang as this seems unusual to me in the Apache community (my ignorance); CouchDB appears to be older than other nosql products I know (now) to be written in Erlang. Whatever their strategy, they at least count Amazon EC2 and Microsoft Azure as hosting partners, indicating an appreciation of Microsoft and !Microsoft worlds - all very important if properly recognizing the middleware value potential (beyond cache or hash table applications) that these types of data stores could have.
Finally, while I don't know the board well, Andy Palmer's guidance looks like it will be valuable. He can bring some guidance vis-a-vis structured data (through VoltDB) to a world that rightly or wrongly may be unfairly branded as KVP hash tables of unstructured data. The need for structure and ecosystem surrounding nosql "databases" is being recognized ... witness Google's efforts with Spanner ... KVP/little structure/need for search-ability motivated Google's investment in the Spanner space. While we all may not need something like Spanner, we probably do need an improving and robust "enterprise" management and interoperability capability in these nosql databases to make it reasonable to incorporate them into modern cloud architectures. The needed structure can come from ease of interoperability and functional richness. It can also come from new capabilities that support conversion of unstructured data to structured data (e.g. indexes, use of NLP to create structured and parsed renderings of things inside of a KVP blob, and plenty of other things that, if put into a roadmap and published, could entice and grow a user base). Cloudant looks like it has a good chance of success ... I will take a closer look at it ...
And look what I found about CouchDB ...
CouchDB comes with a suite of features, such as on-the-fly document transformation and real-time change notifications, that makes web app development a breeze. It even comes with an easy to use web administration console. You guessed it, served up directly out of CouchDB! We care a lot about distributed scaling. CouchDB is highly available and partition tolerant, but is also eventually consistent. And we care a lot about your data. CouchDB has a fault-tolerant storage engine that puts the safety of your data first.