Couchdb Mango Performance vs Map Reduce Views - couchdb

I've just noticed that in the release notes of Couchdb 2.0, it is mentionned that Mango queries are recommended for new applications. It is also mentionned that apparently Mango indexes are from 2x to x10 faster than javascript queries which really surprised me, as such I have a number of questions :
Are Map/Reduce views being phased out ? I'm expecting the answer to be no since it seems to me that Mango does not cover all the use cases of Map/Reduce (the easiest example being Reduce itself), and the flexibility of this querying style seems to be more limited too. But m prefer to ask because of the recommendation :
We recommend all new apps start using Mango as a default.
We know that Map/Reduce views rely on B-trees, but I can't find any insight, in the doc or the mailing list regarding the magic behind Mango. Mango essentially is white magic for me at the minute. Yet I can tell that having an in-depth knowledge of how the javascript views are indexed behind the scenes was massively helpful to avoid pitfalls, naive implementations as well as to optimize performances. Does anyone have any insight on how Mango works ? Are the indexes B-trees too ? When are the indexes updated since there is no longer design documents ? Where do the performance gains come from ? (these gains are counter-intuitive to me, since in my understanding, the performance of javascript queries came from the precomputed nature of Map functions)
What I'm essentially after is on the one hand some insight regarding Mango and on the other hand, an overview of how Mango and Map/Reduce are supposed to live together in the 2.x era.

I recently tried to switch my app over to using Mango queries, with the result of scrapping it completely and switching back to map/reduce. Here are a few of my reasons:
Mango is buggy when dealing with queries that do not exactly specify the index to use. This one drove me batty for a while last weekend. If you don't specify the index, sometimes an alternate index will be selected and return no (or incorrect) results.
Mango performance is not 'magic'. Many types of queries will end up doing in memory searches. Couch will select the best fit index then march through all those records in memory to fit the corner cases. Cloudant hand waves over some of these issues by saying to use 'text' based searches, which aren't available in Couchdb.
As you pointed out, Mango searches simply cannot handle some types of query constructions well. I wouldn't consider my app to be overly complicated yet I ran into several situations where I could not construct a suitable Mango query for the task at hand. A major one here is searching arrays to find tags (for example, searching to see what users are members of a group). Mango cannot index array elements so resorts to doing full scans in memory.
Views have some very powerful features for transformation of search results in the form of Lists. That doesn't exist in Mango.
Your mileage may vary, but just wanted to leave a warning that this is still quite new features.

Answer from a core developer :
Some good questions. I don't think Mango will ever replace Map/Reduce
completely. It is an alternative querying tool. What is great about
the Mango query syntax is that it is a lot easier to understand and
get started. And we can use it in a lot of places outside of just
querying for documents. It can be used for replication filtering and
the changes feed. We hope to soon have support for validation doc
updates as well.
Underneath Mango is using erlang map/reduce. Which means it is
creating a B-tree index just like map/reduce. What makes it faster is
that it is using erlang/native functions to create the B-Tree instead
of javascript. I wrote a blog post a long time ago about the internals
of PouchDB-find [1] which is the mango syntax for PouchDB. It might
help you understand a little more how the internals work. The key
thing to understand is that there is a Map query part which uses the
B-Tree and an in-memory filter. Ideally the less memory filtering you
do the faster your query will be.
I would say that Mango is very much a work in process but the basic
ground work is done. There are definitely things we can improve on.
I've seen it used quite a bit when developers start a new project
because its quick and simple to do basic querying, like find by email
address or find all users with the name "John Rambo".
Hope that helps.
[1] http://www.redcometlabs.com/blog/2015/12/1/a-look-under-the-covers-of-pouchdb-find

I am new to Mango and CouchDB but I think I can provide some insight. Once your index/view is updated, Mango is not any faster. The large performance gain with Mango is when you are creating the index for the first time because couch doesn't need to create a separate couchjs process for this.
I found that Mango works well even when some of your documents are large. Currently with CouchDB 2.0.0, at least with windows, large documents crash the couchjs.exe view server used with Map/Reduce. This is not the case with CouchDB 1.6.1 and is already fixed in the development version https://github.com/apache/couchdb-couch/commit/1659fda5dd1808f55946a637fc26c73913b57e96

Related

How to speed up a search on large collection of text files (1TB)

I have a collection of text files containing anonymised medical data (age, country, symptoms, diagnosis etc). This data goes back for at least 30 years so as you can imagine I have quite a large sized data set. In total I have around 20,000 text files totalling approx. 1TB.
Periodically I will be needing to search these files for occurances of a particular string (not regex). What is the quickest way to search through this data?
I have tried using grep and recursively searching through the directory as follows:
LC_ALL=C fgrep -r -i "searchTerm" /Folder/Containing/Files
The only problem with doing the above is that it takes hours (sometimes half a day!) to search through this data.
Is there a quicker way to search through this data? At this moment I am open to different approaches such as databases, elasticsearch etc. If I do go down the database route, I will have approx. 1 billion records.
My only requirements are:
1) The search will be happening on my local computer (Dual-Core CPU and 8GB RAM)
2) I will be searching for strings (not regex).
3) I will need to see all occurances of the search string and the file it was within.
There are a lot of answers already, I just wanted to add my two cents:
Having this much huge data(1 TB) with just 8 GB of memory will not be good enough for any approach, be it using the Lucene or Elasticsearch(internally uses Lucene) or some grep command if you want faster search, the reason being very simple all these systems hold the data in fastest memory to be able to serve faster and out of 8 GB(25% you should reserve for OS and another 25-50% at least for other application), you are left with very few GB of RAM.
Upgrading the SSD, increasing RAM on your system will help but it's quite cumbersome and again if you hit performance issues it will be difficult to do vertical scaling of your system.
Suggestion
I know you already mentioned that you want to do this on your system but as I said it wouldn't give any real benefit and you might end up wasting so much time(infra and code-wise(so many approaches as mentioned in various answers)), hence would suggest you do the top-down approach as mentioned in my another answer for determining the right capacity. It would help you to identify the correct capacity quickly of whatever approach you choose.
About the implementation wise, I would suggest doing it with Elasticsearch(ES), as it's very easy to set up and scale, you can even use the AWS Elasticsearch which is available in free-tier as well and later on quickly scale, although I am not a big fan of AWS ES, its saves a lot of time of setting up and you can quickly get started if you are much familiar of ES.
In order to make search faster, you can split the file into multiple fields(title,body,tags,author etc) and index only the important field, which would reduce the inverted index size and if you are looking only for exact string match(no partial or full-text search), then you can simply use the keyword field which is even faster to index and search.
I can go on about why Elasticsearch is good and how to optimize it, but that's not the crux and Bottomline is that any search will need a significant amount of memory, CPU, and disk and any one of becoming bottleneck would hamper your local system search and other application, hence advising you to really consider doing this on external system and Elasticsearch really stands out as its mean for distributed system and most popular open-source search system today.
You clearly need an index, as almost every answer has suggested. You could totally improve your hardware but since you have said that it is fixed, I won’t elaborate on that.
I have a few relevant pointers for you:
Index only the fields in which you want to find the search term rather than indexing the entire dataset;
Create multilevel index (i.e. index over index) so that your index searches are quicker. This will be especially relevant if your index grows to more than 8 GB;
I wanted to recommend caching of your searches as an alternative, but this will cause a new search to again take half a day. So preprocessing your data to build an index is clearly better than processing the data as the query comes.
Minor Update:
A lot of answers here are suggesting you to put the data in Cloud. I'd highly recommend, even for anonymized medical data, that you confirm with the source (unless you scraped the data from the web) that it is ok to do.
To speed up your searches you need an inverted index. To be able to add new documents without the need to re-index all existing files the index should be incremental.
One of the first open source projects that introduced incremental indexing is Apache Lucense. It is still the most widely used indexing and search engine although other tools that extend its functionality are more popular nowadays. Elasiticsearch and Solr are both based on Lucense. But as long as you don't need a web frontend, support for analytical querying, filtering, grouping, support for indexing non-text files or an infrastrucutre for a cluster setup over multiple hosts, Lucene is still the best choice.
Apache Lucense is a Java library, but it ships with a fully-functional, commandline-based demo application. This basic demo should already provide all the functionality that you need.
With some Java knowledge it would also be easy to adapt the application to your needs. You will be suprised how simple the source code of the demo application is. If Java shouldn't be the language of your choice, its wrapper for Pyhton, PyLucene may also be an alternative. The indexing of the demo application is already reduced nearly to the minimum. By default no advanced functionlity is used like stemming or optimization for complex queries - features, you most likely will not need for your use-case but which would increase size of the index and indexing time.
I see 3 options for you.
You should really consider upgrading your hardware, hdd -> ssd upgrade can multiply the speed of search by times.
Increase the speed of your search on the spot.
You can refer to this question for various recommendations. The main idea of this method is optimize CPU load, but you will be limited by your HDD speed. The maximum speed multiplier is the number of your cores.
You can index your dataset.
Because you're working with texts, you would need some full text search databases. Elasticsearch and Postgres are good options.
This method requires you more disk space (but usually less than x2 space, depending on the data structure and the list of fields you want to index).
This method will be infinitely faster (seconds).
If you decide to use this method, select the analyzer configuration carefully to match what considered to be a single word for your task (here is an example for Elasticsearch)
Worth covering the topic from at two level: approach, and specific software to use.
Approach:
Based on the way you describe the data, it looks that pre-indexing will provide significant help. Pre-indexing will perform one time scan of the data, and will build a a compact index that make it possible to perform quick searches and identify where specific terms showed in the repository.
Depending on the queries, it the index will reduce or completely eliminate having to search through the actual document, even for complex queries like 'find all documents where AAA and BBB appears together).
Specific Tool
The hardware that you describe is relatively basic. Running complex searches will benefit from large memory/multi-core hardware. There are excellent solutions out there - elastic search, solr and similar tools can do magic, given strong hardware to support them.
I believe you want to look into two options, depending on your skills, and the data (it will help sample of the data can be shared) by OP.
* Build you own index, using light-weight database (sqlite, postgresql), OR
* Use light-weight search engine.
For the second approach, using describe hardware, I would recommended looking into 'glimpse' (and the supporting agrep utility). Glimple provide a way to pre-index the data, which make searches extremely fast. I've used it on big data repository (few GB, but never TB).
See: https://github.com/gvelez17/glimpse
Clearly, not as modern and feature rich as Elastic Search, but much easier to setup. It is server-less. The main benefit for the use case described by OP is the ability to scan existing files, without having to load the documents into extra search engine repository.
Can you think about ingesting all this data to elasticsearch if they have a consistent data structure format ?
If yes, below are the quick steps:
1. Install filebeat on your local computer
2. Install elasticsearch and kibana as well.
3. Export the data by making filebeat send all the data to elasticsearch.
4. Start searching it easily from Kibana.
Fs Crawler might help you in indexing the data into elasticsearch.After that normal elasticsearch queries can you be search engine.
I think if you cache the most recent searched medical data it might help performance wise instead of going through the whole 1TB you can use redis/memcached

What (in_memory) graph DB if modeling data is focused

I am out of ideas and hope to get some useful input. I am using this question to compress my experiences and share them, hoping to inspire some distributors to go the next step with modeling graph databases as a first class question/way.
I've been validating some graph database solutions usable by node.js for a few weeks. My use case is to save interactions of different social user network accounts. The need is to use CPU and memory in the most efficient way.
My most important requirements are:
in_memory (at least for indexing)
open source (and free to use)
same JavaScript/Node.js performance as first class citizen
comfortable query and modeling language
Neo4J
I really like cypher so my best choice would be Neo4j.
But the major issue about Neo4j is the JavaScript access is non-native. It uses the REST-API which is about ten times (10x) slower than direct Java access. So I took a look at node-neo4j-embedded, but it has been inactive for more than two years. It looks like its author isn't active at all (bad sign).
ArangoDB
The really nice core developers of ArangoDB answered to my question about internals. Finally it means JavaScript is first class citizen because native queries can be pushed out of JS. Looking at the open source benchmarks, I think it is fair. But I am afraid they didn't use node-neo4j-embedded for their benchmark. The benchmarks compare the REST-APIs (Edited because of #weinberger comment). I wished they compare the native APIs (maybe someone is snoopy enough and give it a try! - let us know!). Update: As I noticed now, OrientDB has answered the benchmark with a new node.js driver (using Command Cache by starting the server with -Dcommand.cache.enabled=true -Dcommand.cache.minExecutionTime=3, what isn't fair, because it wasn't a query caches benchmark!)
Because I like to use ArangoDB as a graph database I would have 3 choices (source: FAQ):
traverse JS objects
using AQLs graph functions
using the REST API
In general it isn't comfortable like cypher. And I am not sure how to compare and what is the right way modeling data (like Neo4J explains very well). I'd love to have something like this for ArangoDB Graphs. It feels like ArangoDB is focused on graph operations and Neo4J fits more the needs of using graphs if you have more relations than rows (the reason to use graphs instead of relations with joins).
MongoDB
The document based MongoDB isn't optimized for graph operations but latterly has gotten an experimental in_memory storage engine. Also there are some projects either in_memory or graph related but nothing is really compelling. And at this discussion it looks like MongoDB isn't what I like to use.
OrientDB
Because there is a comparison about OrientDB vs. MongoDB available (from OrientDB) I though about to use this one. "OrientDB has a hybrid Document-Graph engine" using SQL. I am a former PHP/MySQL expert. But where is the modeling part ? Their chapter working with graphs is not cypher like. It is like using SQL for Graphs. There is nothing wrong with that, but using cypher before I miss the modeling like feeling.
If someone did a modeling process with OrientDB and Graphs maybe you could write a tutorial like Neo4J had done.
Update: About JavaScript access like first citizen there are news:
"In the next release the speed of this driver will be comparable to the native Java one" The forked node.js driver had bin fixed last days.
Update: Before choosing OrientDB one might want to read article about some issues and discussions linked from there. The article is touching a sensitive issue and should be approached with critical mind. Note from author of this update: I'm new to editing SO and don't have enough reputation to put this to comments. I believe this information is a valid point to discussion, not sure how to place it here according to SO rules.
LokiJS
Before I was looking at Neo4J, ArangoDB and MongoDB, I played around with that JavaScript based in_memory database called LokiJS, what seams to follow the strategy to ignore everything what slows down performance and efficiency. LokiJS is trying to complete the Mongo-Style (RoadMap). The major issue is the bad ability to scale. Of cause it isn't a graph database but it was an interesting solution while the beginning of my project. Also it wasn't a perfect feeling to find all the distributed documentation (maybe they should reboot with GitBook).
Finally LokiJS is a very interesting project at all and I hope they will go forward!
LevelDB
Previously when I wrote my degree paper I was looking at levelDB. Remembering this while writing this post, I searched for LevelDB in_memory and got a promising result called MemDown (see also). I haven't tested this find, but maybe someone has experiences working and modeling for this solution. Maybe it would be the most efficient way if all the others will not fit because I would simply write a lightweight cypher clone with the goal to stay much lightweight as I can do.
Edit: Due to comment, here is a link to LevelGraph. As an idea to implement a CYPHER parser for LevelGraph/LevelDB your starting point would be to compare
Cypher:
CREATE (SUBJECT:"a") - [b:PREDICATE] -> (OBJECT:"c")
RETURN, subject, predicate, object
LevelGraph:
var RETURN = { SUBJECT: "a", PREDICATE: "b", OBJECT: "c" };
db.put(RETURN, function(err) {
// ..
});
Conclusion
As you likely noticed I am not the super hero about graphs. But this is my initial dive into this and I'm trying to get an overview. I assume there are a lot people out there who want to ask the same questions as me but haven't the time. I hope this post will help a lot people and will change by comments and answers to become a well done overview how to modeling data for graphs.
#editors: You are welcome.
#commenters: This is the result of my personal research - if you also have done a journey like me, please answer with a short summary like I have done for each DB I've evaluated (don't forget to target my 4 goals).
The idea to combine node-style performance through any of the native features (e.g. streams) and a high level query language like CYPHER is actually quite neat.
What you likely won't get is any kind of low level API, since this is rather rare with DB authors and, supposedly, not wanted in their design patterns. So, long running tcp connections shall just serve fine.
cypher-stream since to incorporate all of this, while (superficially judged) maintaining a good style.
Since you likely won't get any further with the search, I'd suggest sending him a pull request if any other features are needed :)
You should take a look at Gundb https://github.com/amark/gun
It's open source and has a very active and helpfull lead developer.
Join us at https://gitter.im/amark/gun

Cassandra or mongodb or something else for big online sales site

Currently we are using mongodb as our primary store for big online sales site, and currently we are focusing ourselves on big scalability among multiple machines.
Site backend is written in node.js and we are using mongoose as ODM.
I can see many blog posts which are writing about awesome cassandra DB, and I am starting to think about switching to cassandra. But still I am not sure if this is a really good decision, because I didn't found any good ODM/ORM lib for cassandra and node.js (and writing raw queries can be pain. Also writing good tested ORM/ODM can be time consuming task). So I am not sure how much benefit will I have after this switch. We are using elasticsearch as search engine, and it works excellent in combination with mongodb, and I am asking my self will do also good with cassandra.
If you have any experiance with this, it will be very helpfull.
Thank you!
Cassandra is a very nicely designed database, which can fulfill a lot of scenarios. MongoDB is also a really good DB engine. So let me just compare couple of main bullet points for you.
Always on system
Cassandra is really great when you need to provide 24x7 operations in multiple data centers. If you got more then one datacenter with multiple servers in each of them then Cassandra is great for you. Cassandra can sync writes to more than one datacenter and maintain desired data consistency across complex set ups. Recovery and re-sync is also quite easy.
On the other note MongoDB is easy to operate. If you got one data center and only couple of servers it might be a perfect fit (although global write lock might be a pain over time). In simple deployments it's easy to maintain and monitor.
Scalability
To continue the above statements - Cassandra is linearly scalable. There is, literally, no limit of how big the cluster will be. Your writes will always stay fast, while reads might become more complicated over time - depending on the structure of your data.
Denormalization of data
With Cassandra your writes and reads can be extremely fast if you will create a structure that will reflect what you need to get from your data. There is no query language (well, there is, but it's not exactly SQL) that you can use to reorganize your result set using aggregates, groupings, etc. Yes, some things are doable and some not - that is very specific to Cassandra data model. You will have to implement a lot of things on your own and write the result to the DB - i.e. counters for aggregation, different groupings, etc.
In comparison MongoDB is easy to use, easier to learn and more flexible - both for development (as knowledge curve/efforts goes) and for implementation of business logic (as time/effort is considered). That is - kind of - a reason why there are ORM engines for MongoDB and only couple (very limited) for Cassandra.
To summarize - both DBs are really good... if you will embrace their limitations. If you got only 100GB of data and you need flexible, easy to implement DB engine I would stick to MongoDB, alternatively take a look RethinkDB which have a very similar model and way better (in my personal opinion) clustering/data center replication implementation.
Cassandra is a great option for you if you will need to store TBs of data soon, deploying your apps across multiple data centers while accepting the cost of additional efforts to implement the same features and maintaining similar capabilities.
Don't take it personally that I have used the word only while describing your data set. Yes, it's not big - my company stores more than 20 TB these days... so yeah, 100GB is really not that much...
To stop everyone from pointing that I should compare some other features or point out some other differences between those two - it's just a rough, high level overview on the things I consider relevant to the problem, not a full comparison or analysis of the problem. But feel free to point out what I have missed and I will be happy to include new stuff in this answer...

Does a B Tree work well for auto suggest/auto complete web forms?

Auto suggest/complete fields are used all over the web. Google has appeared to master it given that as soon as one types in a search query, suggestions are returned almost instantaneously.
I'm assuming the framework for achieving this involves a fast, in-memory data store on the web tier. We're building a Grails app based around retail products, so a user may search for Can which should suggest things like Canon, Cancun, etc, and wondering if a Java B-tree cached in memory would suffice for quick auto completes returned as JSON over AJAX. Outside of the jQuery AutoComplete field, do any frameworks and/or libraries exist to facilitate the development of this solution?
Autocomplete is a text matching, information retrieval problem. Implementing your own B-tree and writing your own logic to match words to other words is something you could do. But then you would have to implement Porter Stemming, a Vector Space Model, and a String-edit distance calculation.
...or you could use Lucene and its derivatives, which do a lot of this stuff already. If you really care about the data structures used to store this stuff, you could dive into its source. But I highly doubt writing your own and doing it all yourself would be more maintainable and efficient in the long run.
One of the more popular Grails ecosystem plugins for this is Searchable, which was mentioned in Ledbrook & Smith's Grails in Action. It uses Lucene under the covers, and make sit pretty easy to add full-text search to your domain classes. (For example, check out chapter 8 in GinA or the searchable docs).
The Grails Richui plugin has an autocomplete that I've used in the past. We had it hooked up to hit the database every keystroke (which I would not suggest but our data changed often enough that real-time data was required). If your list of things is pretty static though then it could probably work well for you.
http://grails.org/plugin/richui#AutoComplete

Difference between Document-oriented-DB and Bigtable clones

Can someone give a head-to-head comparison between them?
We are looking for a suitable storage engine for our weblog history data. We looked at Bigtable's paper and understand it is suitable to us well.
However, I also understand that Document-oriented-DB such as MongoDB seems to provide a little more powerful schema power -- i.e, it can model our data as well.
I wonder how nowadays ppl choose a scalable NoSQL DB --- I read enough articles like "we looked at A, B and C, and we decided to use C". But I'd like to see some benchmark number. What I am saying is that if MongoDB and the like can provide same level of performance as Bigtable clones, why don't web companies choose it (preparing to deal with various potentially more complex data problem)?
Thanks,
By the way, I read an article (which convinced me at the moment) saying Cassandra does not fit the M/R operation, any comments?
"I read an article (which convinced me at the moment) saying Cassandra does not fit the M/R operation, any comments?"
Cassandra 0.6 supports map/reduce. Your source was obsolete, apparently.
There's a not too detailed comparison here (notice the .pdf), but it's probably good enough to narrow down your search to 2-3 options.

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