I have recently been introduced to MongoDB and I've come to like a lot (compared to MySQL i used for all projects).
However in some certain situations, storing my data with documents "linking" to each other with simple IDs makes more sense (to reduce duplicated data).
For example, I may have Country and User documents, where a user's location is actually an ID to a Country (since a Country document includes more data, hence duplicating Country data in each user makes no sense).
What I am curious about is.. why would MongoDB be inferior compared to using a proper relationship database?
Is it because I can save transactions by doing joins (as opposed to doing two transactions with MongoDB)?
Thats a good question..!!
I would say there is definitely nothing wrong in using nosql db for the type of data you have described. For simple usecases it will work perfectly well.
The only point is that relational databases have been designed long time back to serve the purpose of storing and querying WELL STRUCTURED DATA.. with proper relations defined. Hence for a large amount of well structured data the performance and the features provided will be a lot more than that provided by a nosql database. Since they are more matured.. its their ball game..!!
On the other hand nosql databases have been designed to handle very large amount of unstructured data and has out of the box support for distributed environment scaling. So its a completely different ball game now..
They basically treat data differently and hence have different strategies / execution plans to fetch a given data..
MongoDB was designed from the ground up to be scalable over multiple servers. When a MongoDB database gets too slow or too big for a single server, you can add additional servers by making the larger collections "sharded". That means that the collection is divided between different servers and each one is responsible for managing a different part of the collection.
The reason why MongoDB doesn't do JOINs is that it is impossible to have JOINs perform well when one or both collections are sharded over multiple nodes. A JOIN requires to compare each entry of table/collection A with each one of table/collection B. There are shortcuts for this when all the data is on one server. But when the data is distributed over multiple servers, large amounts of data need to be compared and synchronized between them. This would require a lot of network traffic and make the operation very slow and expensive.
Is it correct that you have only two tables, country and user. If so, it seems to me the only data duplicated is a foreign key, which is not a big deal. If there is more duplicated, then I question the DB design itself.
In concept, you can do it in NOSQL but why? Just because NOSQL is new? OK, then do it to learn but remember, "if it ain't broke, don't fix it." Apparently the application is already running on relational. If the data is stored in separate documents in MongoDB and you want to interrelate them, you will need to use a link, which will be more work than a join and be slower. You will have to store a link, which would be no better than storing the foreign key. Alternatively, you can embed one document in another in MongoDB, which might even increase duplication.
If it is currently running on MySQL then it is not running on distributed servers, so Mongo's use of distributed servers is irrelevant. You would have to add servers to take advantage of that. If the tables are properly indexed in relational, it will not have to search through large amounts of data.
However, this is not a complex application and you can use either. If the data is stored on an MPP environment with relational, it will run very well and will not need to search to large amounts of data at all. There are two requirements, however, in choosing a partitioning key in MPP: 1. pick one that will achieve an even distribution of data; and 2. pick a key that can allow collocation of data. I recommend you use the same key as the partitioning key (shard key) in both files.
As much as I love MongoDB, I don't see the value in moving your app.
Related
I've tried to figure out what will be the best use cases that suit for Amazon dynamoDB.
When I googled most of the blogs says DyanmoDb will be used only for a large amount of data (BigData).
I'm having a background of relational DB. NoSQL DB is new for me.So when I've tried to relate this to normal relation DB knowledge.
Most of the concepts related to DynamoDb is to create a schema-less table with partition keys/sort keys. And try to query them based on the keys.Also, there is no such concept of stored procedure which makes queries easier and simple.
If we managing such huge Data's doing such complex queries each and every time to retrieve data will be the correct approach without a stored procedure?
Note: I've maybe had a wrong understanding of the concept. So, please anyone clear my thoughts here
Thanks in advance
Jay
In short, systems like DynamoDB are designed to support big data sets (too big to fit a single server) and high write/read throughput by scaling horizontally, as opposed to scaling vertically, which is the more common approach for relational databases historically.
The main approach to support horizontal scalability is by partitioning data, i.e. a data set is split into multiple pieces and distributed among multiple servers. This way it may use more storage and more IOPS, allowing bigger data sets and higher read/write throughput.
However, data partitioning makes it difficult to support complex queries, such as joins etc., as data is distributed among multiple physical servers. As for stored procedures, they are not supported for the same reason - historically the idea behind stored procedures is data locality, i.e. they run on the server near the data without network operations, however, if data is distributed among multiple servers, this benefit disappears (at least in the form of stored procedure).
Therefore the most efficient way to query data from such systems is by record key, as data partitioning is based on a key and it's easy to figure out where a record lives physically for a given key. While many such systems also support secondary indexes, they are usually restricted in some way or expensive and may not be enough to satisfy requirements in a complex software solution. A quite common approach is to have a complementary indexing/query solution (I've seen solutions based on Elasticsearch and Solr), which allows running complex queries over some fragments of records to figure out a record key, which then used to load the record.
If, as an example, you have a blogging website done with MongoDB to store data
Is it better to have a database per blogger? given that their blogs and comments are completely independent from other bloggers. Or just lump everything together? or it doesn't make too much difference?
I'm imagining the same web app (not independent webs/urls per blogger) is used by all bloggers. So when someone logs in / accesses the blog the code would find the right database to use and haul data out it.
Does this have any downsides? is this normal for handling these kinds of things?
I am making plenty of assumptions about your needs. But, generally, there are 3 paths to multi-tenant apps in MongoDB:
Single collection per customer; never, ever do this.
Single database per customer. Good. You will trade off free space if your product is on the freemium model. Either way, you will want to run with "smallfiles" option. As stated, you will build the routing system for your environment. Thus, you will want to connect to the proper database for the proper customer.
customer_id key per document + path slug. Good. The trade off here is recovery of free space. Traditionally, MongoDB does not recover space used by deleted documents. Thus customers creating and deleting blog posts would create unused space. By using 'usePowerOf2Sizes' collections, you will recover disk space of deleted documents. However, 'usePowerOf2Sizes' creates bloated padding space.
To get over the disk space padding, take a look at the compression used here: http://blog.appsignal.com/blog/2013/07/30/taming-mongodb-disk-usage.html
Recap, I would recommend using customer_id plus the compression. It gives you the best of both worlds.
As stated in the comments under the original question, there's really no performance benefit to splitting up your MongoDB store into separate databases per blogger, due to the overhead of having each database and minimum storage.
On the flipside: You are going to make some cross-user analysis more difficult for yourself. As a very simple example, based on your blogging example: Imagine you want to look at average post count per user. This is pretty simple if your users (and posts) are in the same database (typically in the same collections), and you can likely use the aggregation framework for this task. This task will not be so straightforward with an unbounded number of databases, where you'll need to first enumerate all databases, then perform your aggregations/averaging once per database. This could end up being a slower operation than within a single-database architecture.
Having said all that: You still might have some reason to split data across databases. Maybe you have to separate data due to legal reasons, or to ensure customers that their sensitive data won't be commingled with other companies' data. Maybe your customer needs full read/write access to their database, and so you use per-database configuration as a security boundary. I'm sure there are other reasons as well...
It is perfectly normal to allocate 100's of databases if that is all you will see.
Database separation can have many benefits. They can be sharded independantly, since sharding occurs on database level. Databases also have the upside of being completely isolated instances (including locks) of the data within them (good example: space allocation occurs on database level).
This means they can be moved around the network as users data is accessed more and since a single users data might not be that big it would be easier than moving all of your users data to a more powerful node.
However, you must consider the problematic sides in the application of managing the connections to each database. There will be over head on it and you will need to have far more complex coding than what is considered standard.
Considering space, you will not see a drastic usage of space. The most problematic part of using separate databases is the journal allocation. Every collection you use in separate databases will also, of course, pre-allocate itself but this is actually considered one of the upsides to using database separation (movement of databases between nodes, isolation).
So the space problem is really only a problem if your scenario makes it one.
is this normal for handling these kinds of things?
For a normal blogger site, no, and I do not know enough about the complexities of your scenario to say any different. Normal operation would be to lump everything together, since you could see into the region of 1,000's maybe 1,000,000's of users and database separation just won't scale over that very well.
Is there any sense to use mongodb in a system with great amount of entities (50+) connected to each other, for example in CRM. Any "success stories"?
There is a need of intensive writing and fast selection from high number of records for the some kind of analytics system.
It is definitely hard to provide a recommendation with such open question; however, you can analyze some of the advantages of MongoDB over other database, most likely you are considering Mongo as an alternative to a relational database like Oracle or SQL Server.
From http://mongodb.org you can see the main characteristics...
Document Oriented Storage: Which basically means you can have a single or multiple documents representing your data structures. One very important think here is that the schema is dynamic, that is you can add more attributes without having to change your database. Pretty useful for adding flexibility to your system.
Full index support: We wouldn't expect any less than full support for indices, right?
Replication and High availability; Sharding: Very critical elements for availability, disaster recovery, and to guarantee the
ability to grow with your system.
Querying: Again, pretty critical requirement. Need to make sure you account for the dynamic schema. You will need to consider in
your queries that some attributes are not defined for all documents
(remember dynamic schema?).
Map/Reduce: Very useful for
analytics. Recommended for aggregating large amounts of data.
Should be used offline, meaning, you don't run a live query against a
map/reduce function, otherwise you will be sitting for a while
waiting. But it is great to run batch analytics on your system.
GridFS: A great way of storing binary data. Automatically generates MD5's for your files, splits them in chunks, and can add
metadata. Your files will stay with your database.
Also, the Geolocation indices are great. You can define lon,lat attributes and do searches on those.
Now it is up to you to see if these features are good for your needs, or you rather stay with a well know relational system.
Before jumping into a solution you should experiment and build some prototypes. You will see very early what challenges you'll have in your design.
Hope this helps.
I am working on a project were we are batch loading and storing huge volume of data in Oracle database which is constantly getting queried via Hibernate against this 100+ million records table (the reads are much more frequent than writes).
To speed things up we are using Lucene for some of queries (especially geo bounding box queries) and Hibernate second level cache but thats still not enough. We still have bottleneck in Hibernate queries against Oracle (we dont cache 100+ million table entities in Hibernate second level cache due to lack of that much memory).
What additional NoSQL solutions (apart from Lucene) I can leverage in this situation?
Some options I am thinking of are:
Use distributed ehcache (Terracotta) for Hibernate second level to leverage more memory across machines and reduce duplicate caches (right now each VM has its own cache).
To completely use in memory SQL database like H2 but unfortunately those solutions require loading 100+ mln tables into single VM.
Use Lucene for querying and BigTable (or distributed hashmap) for entity lookup by id.
What BigTable implementation will be suitable for this? I was considering HBase.
Use MongoDB for storing data and for querying and lookup by id.
recommending Cassandra with ElasticSearch for a scalable system (100 million is nothing for them). Use cassandra for all your data and ES for ad hoc and geo queries. Then you can kill your entire legacy stack. You may need a MQ system like rabbitmq for data sync between Cass. and ES.
It really depends on your data sets. The number one rule to NoSQL design is to define your query scenarios first. Once you really understand how you want to query the data then you can look into the various NoSQL solutions out there. The default unit of distribution is key. Therefore you need to remember that you need to be able to split your data between your node machines effectively otherwise you will end up with a horizontally scalable system with all the work still being done on one node (albeit better queries depending on the case).
You also need to think back to CAP theorem, most NoSQL databases are eventually consistent (CP or AP) while traditional Relational DBMS are CA. This will impact the way you handle data and creation of certain things, for example key generation can be come trickery.
Also remember than in some systems such as HBase there is no indexing concept. All your indexes will need to be built by your application logic and any updates and deletes will need to be managed as such. With Mongo you can actually create indexes on fields and query them relatively quickly, there is also the possibility to integrate Solr with Mongo. You don’t just need to query by ID in Mongo like you do in HBase which is a column family (aka Google BigTable style database) where you essentially have nested key-value pairs.
So once again it comes to your data, what you want to store, how you plan to store it, and most importantly how you want to access it. The Lily project looks very promising. THe work I am involved with we take a large amount of data from the web and we store it, analyse it, strip it down, parse it, analyse it, stream it, update it etc etc. We dont just use one system but many which are best suited to the job at hand. For this process we use different systems at different stages as it gives us fast access where we need it, provides the ability to stream and analyse data in real-time and importantly, keep track of everything as we go (as data loss in a prod system is a big deal) . I am using Hadoop, HBase, Hive, MongoDB, Solr, MySQL and even good old text files. Remember that to productionize a system using these technogies is a bit harder than installing Oracle on a server, some releases are not as stable and you really need to do your testing first. At the end of the day it really depends on the level of business resistance and the mission-critical nature of your system.
Another path that no one thus far has mentioned is NewSQL - i.e. Horizontally scalable RDBMSs... There are a few out there like MySQL cluster (i think) and VoltDB which may suit your cause.
Again it comes to understanding your data and the access patterns, NoSQL systems are also Non-Rel i.e. non-relational and are there for better suit to non-relational data sets. If your data is inherently relational and you need some SQL query features that really need to do things like Cartesian products (aka joins) then you may well be better of sticking with Oracle and investing some time in indexing, sharding and performance tuning.
My advice would be to actually play around with a few different systems. Look at;
MongoDB - Document - CP
CouchDB - Document - AP
Redis - In memory key-value (not column family) - CP
Cassandra - Column Family - Available & Partition Tolerant (AP)
HBase - Column Family - Consistent & Partition Tolerant (CP)
Hadoop/Hive
VoltDB - A really good looking product, a relation database that is distributed and might work for your case (may be an easier move). They also seem to provide enterprise support which may be more suited for a prod env (i.e. give business users a sense of security).
Any way thats my 2c. Playing around with the systems is really the only way your going to find out what really works for your case.
As you suggest MongoDB (or any similar NoSQL persistence solution) is an appropriate fit for you. We've run tests with significantly larger data sets than the one you're suggesting on MongoDB and it works fine. Especially if you're read heavy MongoDB's sharding and/or distributing reads across replicate set members will allow you to speed up your queries significantly. If your usecase allows for keeping your indexes right balanced your goal of getting close to 20ms queries should become feasable without further caching.
You should also check out the Lily project (lilyproject.org). They have integrated HBase with Solr. Internally they use message queues to keep Solr in sync with HBase. This allows them to have the speed of solr indexing (sharding and replication), backed by a highly reliable data storage system.
you could group requests & split them specific to a set of data & have a single (or a group of servers) process that, here you can have the data available in the cache to improve performance.
e.g.,
say, employee & availability data are handled using 10 tables, these can be handled b a small group of server (s) when you configure hibernate cache to load & handle requests.
for this to work you need a load balancer (which balances load by business scenario).
not sure how much of it can be implemented here.
At the 100M records your bottleneck is likely Hibernate, not Oracle. Our customers routinely have billions of records in the individual fact tables of our Oracle-based data warehouse and it handles them fine.
What kind of queries do you execute on your table?
I would like to test the NoSQL world. This is just curiosity, not an absolute need (yet).
I have read a few things about the differences between SQL and NoSQL databases. I'm convinced about the potential advantages, but I'm a little worried about cases where NoSQL is not applicable. If I understand NoSQL databases essentially miss ACID properties.
Can someone give an example of some real world operation (for example an e-commerce site, or a scientific application, or...) that an ACID relational database can handle but where a NoSQL database could fail miserably, either systematically with some kind of race condition or because of a power outage, etc ?
The perfect example will be something where there can't be any workaround without modifying the database engine. Examples where a NoSQL database just performs poorly will eventually be another question, but here I would like to see when theoretically we just can't use such technology.
Maybe finding such an example is database specific. If this is the case, let's take MongoDB to represent the NoSQL world.
Edit:
to clarify this question I don't want a debate about which kind of database is better for certain cases. I want to know if this technology can be an absolute dead-end in some cases because no matter how hard we try some kind of features that a SQL database provide cannot be implemented on top of nosql stores.
Since there are many nosql stores available I can accept to pick an existing nosql store as a support but what interest me most is the minimum subset of features a store should provide to be able to implement higher level features (like can transactions be implemented with a store that don't provide X...).
This question is a bit like asking what kind of program cannot be written in an imperative/functional language. Any Turing-complete language and express every program that can be solved by a Turing Maching. The question is do you as a programmer really want to write a accounting system for a fortune 500 company in non-portable machine instructions.
In the end, NoSQL can do anything SQL based engines can, the difference is you as a programmer may be responsible for logic in something Like Redis that MySQL gives you for free. SQL databases take a very conservative view of data integrity. The NoSQL movement relaxes those standards to gain better scalability, and to make tasks that are common to Web Applications easier.
MongoDB (my current preference) makes replication and sharding (horizontal scaling) easy, inserts very fast and drops the requirement for a strict scheme. In exchange users of MongoDB must code around slower queries when an index is not present, implement transactional logic in the app (perhaps with three phase commits), and we take a hit on storage efficiency.
CouchDB has similar trade-offs but also sacrifices ad-hoc queries for the ability to work with data off-line then sync with a server.
Redis and other key value stores require the programmer to write much of the index and join logic that is built in to SQL databases. In exchange an application can leverage domain knowledge about its data to make indexes and joins more efficient then the general solution the SQL would require. Redis also require all data to fit in RAM but in exchange gives performance on par with Memcache.
In the end you really can do everything MySQL or Postgres do with nothing more then the OS file system commands (after all that is how the people that wrote these database engines did it). It all comes down to what you want the data store to do for you and what you are willing to give up in return.
Good question. First a clarification. While the field of relational stores is held together by a rather solid foundation of principles, with each vendor choosing to add value in features or pricing, the non-relational (nosql) field is far more heterogeneous.
There are document stores (MongoDB, CouchDB) which are great for content management and similar situations where you have a flat set of variable attributes that you want to build around a topic. Take site-customization. Using a document store to manage custom attributes that define the way a user wants to see his/her page is well suited to the platform. Despite their marketing hype, these stores don't tend to scale into terabytes that well. It can be done, but it's not ideal. MongoDB has a lot of features found in relational databases, such as dynamic indexes (up to 40 per collection/table). CouchDB is built to be absolutely recoverable in the event of failure.
There are key/value stores (Cassandra, HBase...) that are great for highly-distributed storage. Cassandra for low-latency, HBase for higher-latency. The trick with these is that you have to define your query needs before you start putting data in. They're not efficient for dynamic queries against any attribute. For instance, if you are building a customer event logging service, you'd want to set your key on the customer's unique attribute. From there, you could push various log structures into your store and retrieve all logs by customer key on demand. It would be far more expensive, however, to try to go through the logs looking for log events where the type was "failure" unless you decided to make that your secondary key. One other thing: The last time I looked at Cassandra, you couldn't run regexp inside the M/R query. Means that, if you wanted to look for patterns in a field, you'd have to pull all instances of that field and then run it through a regexp to find the tuples you wanted.
Graph databases are very different from the two above. Relations between items(objects, tuples, elements) are fluid. They don't scale into terabytes, but that's not what they are designed for. They are great for asking questions like "hey, how many of my users lik the color green? Of those, how many live in California?" With a relational database, you would have a static structure. With a graph database (I'm oversimplifying, of course), you have attributes and objects. You connect them as makes sense, without schema enforcement.
I wouldn't put anything critical into a non-relational store. Commerce, for instance, where you want guarantees that a transaction is complete before delivering the product. You want guaranteed integrity (or at least the best chance of guaranteed integrity). If a user loses his/her site-customization settings, no big deal. If you lose a commerce transation, big deal. There may be some who disagree.
I also wouldn't put complex structures into any of the above non-relational stores. They don't do joins well at-scale. And, that's okay because it's not the way they're supposed to work. Where you might put an identity for address_type into a customer_address table in a relational system, you would want to embed the address_type information in a customer tuple stored in a document or key/value. Data efficiency is not the domain of the document or key/value store. The point is distribution and pure speed. The sacrifice is footprint.
There are other subtypes of the family of stores labeled as "nosql" that I haven't covered here. There are a ton (122 at last count) different projects focused on non-relational solutions to data problems of various types. Riak is yet another one that I keep hearing about and can't wait to try out.
And here's the trick. The big-dollar relational vendors have been watching and chances are, they're all building or planning to build their own non-relational solutions to tie in with their products. Over the next couple years, if not sooner, we'll see the movement mature, large companies buy up the best of breed and relational vendors start offering integrated solutions, for those that haven't already.
It's an extremely exciting time to work in the field of data management. You should try a few of these out. You can download Couch or Mongo and have them up and running in minutes. HBase is a bit harder.
In any case, I hope I've informed without confusing, that I have enlightened without significant bias or error.
RDBMSes are good at joins, NoSQL engines usually aren't.
NoSQL engines is good at distributed scalability, RDBMSes usually aren't.
RDBMSes are good at data validation coinstraints, NoSQL engines usually aren't.
NoSQL engines are good at flexible and schema-less approaches, RDBMSes usually aren't.
Both approaches can solve either set of problems; the difference is in efficiency.
Probably answer to your question is that mongodb can handle any task (and sql too). But in some cases better to choose mongodb, in others sql database. About advantages and disadvantages you can read here.
Also as #Dmitry said mongodb open door for easy horizontal and vertical scaling with replication & sharding.
RDBMS enforce strong consistency while most no-sql are eventual consistent. So at a given point in time when data is read from a no-sql DB it might not represent the most up-to-date copy of that data.
A common example is a bank transaction, when a user withdraw money, node A is updated with this event, if at the same time node B is queried for this user's balance, it can return an outdated balance. This can't happen in RDBMS as the consistency attribute guarantees that data is updated before it can be read.
RDBMs are really good for quickly aggregating sums, averages, etc. from tables. e.g. SELECT SUM(x) FROM y WHERE z. It's something that is surprisingly hard to do in most NoSQL databases, if you want an answer at once. Some NoSQL stores provide map/reduce as a way of solving the same thing, but it is not real time in the same way it is in the SQL world.