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I got two opinions about NoSQL from my friend.
First: Use NoSQL to boost performance and save occasional updated data. Still use sql to save all important dan transaction data.
Second: Don't use NoSQL if you didn't really need it. Use it if you really save big data.
I've used NoSQL and its really fast when selecting data.
I want to know, is first opinion only enough for implementing NoSQL? What all of you think about these?
NOTE: In my case, it still running well with SQL. I want to add NoSQL for improving data reading speed. so it will work alongside.
Is it worth it to use NoSQL this early?
Thanks in advance
It depends on what you are designing.
From my experience scaling out data collection I have found traditional relational storage to be a bottleneck in terms of its inability to scale out over multiple nodes when a databases gets very large. Sure it scales up but this becomes cost prohibitive at some point. In this scenario it would therefore depend on your medium to long term data storage projections. The solution for me was therefore mixture of relational storage for data that may be updated frequently and noSQL (document storage) for data the has a fast rate of growth that is generally not updated post write.
Things to take into account:
Queries
SQL relational storage supports a growing subset languages for queries, as well as a wide range of filters, sorting options, and projections and index queries. NoSQL does all this as well, but SQL can often go beyond it, allowing powerful aggregations of your data as well, beyond what NoSQL can do.
Transactions
Transactions are important because they ensure that you have atomically made changes to your database. Many NoSQL platforms don’t support transactions, so be aware of this feature when you’re figuring out which to use, and what your own needs are.
Consistency
MySQL platforms often use a single master to guarantee strong consistency in your database. These use synchronous replication to ensure you don’t lose important changes queued up to the master. NoSQL, by contrast, does replication of entity groups without a master, so that data is strong within an entity group, and is eventually updated across all groups. The better option depends on the constraints and needs of your database.
Scalability
For years, database administrators relied on scaling up, buying bigger servers as database load increased. However, as transaction rates and demands on the databases continue to expand immensely, emphasis is on scaling out instead. Scaling out is distributing databases across multiple hosts, and that’s something NoSQL does better than standard SQL. They’re designed for optimal use on scaled out databases.
Management
NoSQL databases are generally designed to require less management overall. Repairs are often automatic, and data distribution and simpler data models contribute to less administration required overall. However, you’ve also got less support when there’s a problem. SQL platforms often have vendors waiting to supply support to enterprises.
Schema
Regular SQL platforms often have strictly enforced rules for a schema change, to stave off user-created typos that can put faults in your query. NoSQL platforms will have their own mechanisms for combating this.
Hope that helps.
NoSQL scores over SQL in below areas
It support semi-structured data and volatile data. You can change the structure at any time
It does not have schema
Read/Write through put is very high
Horizontal scalability is easily achieved - Add cheaper hardware and provide right replication factor
Will support Bigdata in volumes of Terra Bytes & Peta Bytes by using cheaper hardware
Good support for Analytic tools on top of Bigdata, especially Hadoop/Hbase family
In memory caching option is available to increase the performance of queries
Faster development life cycles for developers
When you should not use NoSQL and go for SQL
If you require business critical transaction with ACID properties i.e where Consistency is key & Eventual consistency is not an option
If you have heavy aggregation queries spanning multiple entities
In summary, you have to use right technology for right business use case. i.e combination of SQL and NoSQL
Regarding your queries:
Use SQL for business critical transactions. If your SQL is scaling for your business requirements, use SQL.
Use NoSQL for huge volumes of data in magnitudes of Tera/Peta bytes with variety of data , where SQL can't handle that volume & variety.
As others pointed out both SQL and NoSQL (Not only SQL) have their advantages.
There is often temptation to use both side by side and get maximum out of it. Something referred to as Polyglot persistence
Is it a good idea? Sometimes, yes.
Should I do it?
While it may have benefits, the trade off comes with maintenance of multiple stores (note: they would have different ways of database management).
Also the data sync is a bigger one if you are planning this for same transactional system.
If the data you are going to store (in sql and no-sql databases) can be logically separated then you might be ok. But in case they are closely related then you are going to have tough time keeping them consistent.
Overall when i evaluated this option, i came to conclude that it would work only when you can logically partition the data. Another use case may be using Nosql for Analytics and continue with sql for transaction system.
Going back to your use case, did you try JSON storage within your sql database. It may give you benefit of performance without much tradeoffs.
I've been trying to learn more about database scaling in a distributed system, and I am stuck in between RDBMS and NoSQL.
Some articles online suggest that NoSQL is the solution to modern Big Data. Others say NoSQL is just a hype and RDBMS can be just as scalable with good design, and it provides good data structure.
Instead of reading others' opinions, I'd love to judge the two myself, but I do not understand exactly what is required for a scalable RDBMS and a scalable NoSQL.
I've done a bit more readings on RDBMS, and it seems that the solution requires leveraging memcache and sharding to reduce database size and the number of DB queries. Are there other tricks? Can you still use tables with many columns? Or use less columns and more joins?
As for NoSQL, I've read a little about MongoDB. I understand that it encourages data aggregation. But how does that make it more scalable? I'm also starting to learn Cassandra because I read that it scales much better than MongoDB, but I have no idea how it is more scalable.
I would very much appreciate a basic (or advanced, if you have the patience to type it out) condensed and down-to-the-core explanation on scaling RDBMS and NoSQL, or good articles online or books that explain the topic. :)
I won't cover ways you can scale by implementing things on your own and putting a memcache server in between, ... I'll just cover what comes right out of the box...
Let's start first with RDBMS:
I think setting up an RDBMS cluster is more complicated than a NoSQL cluster, but that's just my opinion. Usually what you have is one Master and multiple Slaves. You have to send all your writes to the master and can read from any slave you want. Since you have RDBMS and ACID, the system should somehow guarantee you, that you won't read old data. So the thing here is, that you assume that your application writes once and reads often (as it's usually the case). For those purposes, one Server for read/write and multiple servers for read is great. The problem is if you'r writes are so often that you can't keep up with them anymore on the one machine. That is your bottleneck. Additionally to the build in solutions from Oracle for instance - which are huge - there is also http://www.scalearc.com/ which can cache queries, ... and handle the scaling for you.
NoSQL:
There is no 1 NoSQL schema which is implemented by all the DBs. Every system is a bit different. MongoDB for instance is quite similar to RDBMS, it also has only one Master and several slaves to which it can replicate data, but additionally you can also create shards. Data is split between shards, and replicated to slaves. So you could have multiple different masters which are responsible for smaller parts. Afterwards when you read, you can choose if you want to read from multiple slaves, from the master or from any slave - depending how urgently you need the latest data.
Cassandra on the other hand works totally differently. I'm not sure if you can write to multiple servers or how it works, but basically the servers keep a log of all the writes. So even if they can't process the writes immediately, they are stored in a log, to still give you a fast response. Afterwards when you read, you can say again how urgently you want to have the new data, and if you really want the latest latest data, Cassandra will need to check the log, if there are any updates written, and it will cost you a lot of time.
Key-Value stores like ElasticSearch, CouchDB, CouchBase work again differently. Here the of the item is hashed, and based on the hash, sent to one node which will be responsible for it. This way, when you read after the key was written, you get again up to date information, because you'll read from the same node. The idea of this design is, that no one single key will be of everyone's interest, but the load will be distributed. These are also the DBs which I think scale the best, and make it the easiest to add more servers to the cluster, but you loose the power of complex queries, like you have it in MongoDB and Cassandra - and of course RDBMS. ElasticSearch has some simple search queries, and CouchDB and CouchBase have only Views which are produced by MapReduce, where you can get data which you want, if it fits the view. Otherwise you can only access it by the key.
http://kkovacs.eu/cassandra-vs-mongodb-vs-couchdb-vs-redis - is a very comprehensive summary of the most common NoSQL DBs, what are their strengths and weaknesses, and the most common usage scenarios.
In the end, the question is also, why do you want to scale? how many records are you going to have in the database? Few millions is not a problem at all. Few hundred millions is also not a problem for most of the RDBMS on a powerful enough server. And if designed the DB and it's indices properly even a billion records per year should be still fine.
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There has been a lot of talk related to Cassandra lately.
Twitter, Digg, Facebook, etc all use it.
When does it make sense to:
use Cassandra,
not use Cassandra, and
use a RDMS instead of Cassandra.
There is nothing like a silver bullet, everything is built to solve specific problems and has its own pros and cons. It is up to you, what problem statement you have and what is the best fitting solution for that problem.
I will try to answer your questions one by one in the same order you asked them. Since Cassandra is based on the NoSQL family of databases, it's important you understand why use a NoSQL database before I answer your questions.
Why use NoSQL
In the case of RDBMS, making a choice is quite easy because all the databases like MySQL, Oracle, MS SQL, PostgreSQL in this category offer almost the same kind of solutions oriented toward ACID properties. When it comes to NoSQL, the decision becomes difficult because every NoSQL database offers different solutions and you have to understand which one is best suited for your app/system requirements. For example, MongoDB is fit for use cases where your system demands a schema-less document store. HBase might be fit for search engines, analyzing log data, or any place where scanning huge, two-dimensional join-less tables is a requirement. Redis is built to provide In-Memory search for varieties of data structures like trees, queues, linked lists, etc and can be a good fit for making real-time leaderboards, pub-sub kind of system. Similarly there are other databases in this category (Including Cassandra) which are fit for different problem statements. Now lets move to the original questions, and answer them one by one.
When to use Cassandra
Being a part of the NoSQL family, Cassandra offers a solution for problems where one of your requirements is to have a very heavy write system and you want to have a quite responsive reporting system on top of that stored data. Consider the use case of Web analytics where log data is stored for each request and you want to built an analytical platform around it to count hits per hour, by browser, by IP, etc in a real time manner. You can refer to this blog post to understand more about the use cases where Cassandra fits in.
When to Use a RDMS instead of Cassandra
Cassandra is based on a NoSQL database and does not provide ACID and relational data properties. If you have a strong requirement for ACID properties (for example Financial data), Cassandra would not be a fit in that case. Obviously, you can make a workaround for that, however you will end up writing lots of application code to simulate ACID properties and will lose on time to market badly. Also managing that kind of system with Cassandra would be complex and tedious for you.
When not to use Cassandra
I don't think it needs to be answered if the above explanation makes sense.
When evaluating distributed data systems, you have to consider the CAP theorem - you can pick two of the following: consistency, availability, and partition tolerance.
Cassandra is an available, partition-tolerant system that supports eventual consistency. For more information see this blog post I wrote: Visual Guide to NoSQL Systems.
Cassandra is the answer to a particular problem: What do you do when you have so much data that it does not fit on one server ? How do you store all your data on many servers and do not break your bank account and not make your developers insane ? Facebook gets 4 Terabyte of new compressed data EVERY DAY. And this number most likely will grow more than twice within a year.
If you do not have this much data or if you have millions to pay for Enterprise Oracle/DB2 cluster installation and specialists required to set it up and maintain it, then you are fine with SQL database.
However Facebook no longer uses cassandra and now uses MySQL almost exclusively moving the partitioning up in the application stack for faster performance and better control.
The general idea of NoSQL is that you should use whichever data store is the best fit for your application. If you have a table of financial data, use SQL. If you have objects that would require complex/slow queries to map to a relational schema, use an object or key/value store.
Of course just about any real world problem you run into is somewhere in between those two extremes and neither solution will be perfect. You need to consider the capabilities of each store and the consequences of using one over the other, which will be very much specific to the problem you are trying to solve.
Besides the answers given above about when to use and when not to use Cassandra, if you do decide to use Cassandra you may want to consider not using Cassandra itself, but one of the its many cousins out there.
Some answers above already pointed to various "NoSQL" systems which share many properties with Cassandra, with some small or large differences, and may be better than Cassandra itself for your specific needs.
Additionally, recently (several years after this question was originally asked), a Cassandra clone called Scylla (see https://en.wikipedia.org/wiki/Scylla_(database)) was released. Scylla is an open-source re-implementation of Cassandra in C++, which claims to have significantly higher throughput and lower latencies than the original Java Cassandra, while being mostly compatible with it (in features, APIs, and file formats). So if you're already considering Cassandra, you may want to consider Scylla as well.
I will focus here on some of the important aspects which can help you to decide if you really need Cassandra. The list is not exhaustive, just some of the points which I have at top of my mind-
Don't consider Cassandra as the first choice when you have a strict requirement on the relationship (across your dataset).
Cassandra by default is AP system (of CAP). But, it supports tunable consistency which means it can be configured to support as CP as well. So don't ignore it just because you read somewhere that it's AP and you are looking for CP systems. Cassandra is more accurately termed “tuneably consistent,” which means it allows you to easily decide the level of consistency you require, in balance with the level of availability.
Don't use Cassandra if your scale is not much or if you can deal with a non-distributed DB.
Think harder if your team thinks that all your problems will be solved if you use distributed DBs like Cassandra. To start with these DBs is very simple as it comes with many defaults but optimizing and mastering it for solving a specific problem would require a good (if not a lot) amount of engineering effort.
Cassandra is column-oriented but at the same time each row also has a unique key. So, it might be helpful to think of it as an indexed, row-oriented store. You can even use it as a document store.
Cassandra doesn't force you to define the fields beforehand. So, if you are in a startup mode or your features are evolving (as in agile) - Cassandra embraces it. So better, first think about queries and then think about data to answer them.
Cassandra is optimized for really high throughput on writes. If your use case is read-heavy (like cache) then Cassandra might not be an ideal choice.
Right. It makes sense to use Cassandra when you have a huge amount of data, a huge number of queries but very little variety of queries. Cassandra basically works by partitioning and replicating. If all your queries will be based on the same partition key, Cassandra is your best bet. If you get a query on an attribute that is not the partition key, Cassandra allows you to replicate the whole data with a new partition key. So now you have 2 replicas of the same data with 2 different partition keys.
Which brings me to your next question. When not to use Cassandra. As I mentioned, Cassandra scales by replicating the complete database for every new partitioning key. But you can't keep making new copies again and again. So when you have a high variety in queries i.e. each query has a different column in the where clause, Cassandra is not a good option.
Now for the third question. The whole point of using RDBMS is when you want the ACID properties. If you are building something like a payment service and want each transaction to be isolated, each transaction to either complete or not happen at all, changes to be persistent despite system failure, and the money to be consistent across bank accounts before and after the transaction completes, an RDBMS is the only option that will help you achieve this.
This article actually explains the whole thing, especially when to use Cassandra or not (as opposed to some other NoSQL option) part of the question -> Choosing the best Database. Do check it out.
EDIT: To answer the question in the comments by proximab, when we think of banking systems we immidiately think "ACID is the best solution". But even banking systems are made up of several subsystems that might not even be dealing with any transaction related data like account holder's personal information, account statements, credit card details, credit histories, etc.
All of this information needs to be stored in some database or the another. Now if you store the account related information like account balance, that is something that needs to be consistent at all times. For example, if you try to send money from account A to account B, then the money that disappears from account A should instantaneousy show up in account B, and it cannot be present in both accounts at the same time. This system cannot be inconsistant at any point. This is where ACID is of utmost importance.
On the other hand if you are saving credit card details or credit histories, that should not get into the wrong hands, then you need something that allows access only to authorised users. That I believe is supported by Cassandra. That said, data like credit history and credit card transactions, I think that is an ever increasing data. Also there is only so much yo can query on this data i.e. it has a very finite number of queries. These two conditions make Cassandra a perfect solution.
Talking with someone in the midst of deploying Cassandra, it doesn't handle the many-to-many well. They are doing a hack job to do their initial testing. I spoke with a Cassandra consultant about this and he said he wouldn't recommend it if you had this problem set.
You should ask your self the following questions:
(Volume, Velocity) Will you be writing and reading TONS of information , so much information that no one computer could handle the writes.
(Global) Will you need this writing and reading capability around the world so that the writes in one part of the world are accessible in another part of the world?
(Reliability) Do you need this database to be up and running all the time and never go down regardless of which Cloud, which country, whether it's VM , Container, or Bare metal?
(Scale-ability) Do you need this database to be able to continue to grow easily and scale linearly
(Consistency) Do you need TUNABLE consistency where some writes can happen asynchronously where as others need to be certified?
(Skill) Are you willing to do what it takes to learn this technology and the data modeling that goes with creating a globally distributed database that can be fast for everyone, everywhere?
If for any of these questions you thought "maybe" or "no," you should use something else. If you had "hell yes" as an answer to all of them, then you should use Cassandra.
Use RDBMS when you can do everything on one box. It's probably easier than most and anyone can work with it.
Heavy single query vs. gazillion light query load is another point to consider, in addition to other answers here. It's inherently harder to automatically optimize a single query in a NoSql-style DB. I've used MongoDB and ran into performance issues when trying to calculate a complex query. I haven't used Cassandra but I expect it to have the same issue.
On the other hand, if your load is expected to be that of very many small queries, and you want to be able to easily scale out, you could take advantage of eventual consistency that is offered by most NoSql DBs. Note that eventual consistency is not really a feature of a non-relational data model, but it is much easier to implement and to set up in a NoSql-based system.
For a single, very heavy query, any modern RDBMS engine can do a decent job parallelizing parts of the query and take advantage of as much CPU and memory you throw at it (on a single machine). NoSql databases don't have enough information about the structure of the data to be able to make assumptions that will allow truly intelligent parallelization of a big query. They do allow you to easily scale out more servers (or cores) but once the query hits a complexity level you are basically forced to split it apart manually to parts that the NoSql engine knows how to deal with intelligently.
In my experience with MongoDB, in the end because of the complexity of the query there wasn't much Mongo could do to optimize it and run parts of it on multiple data. Mongo parallelizes multiple queries but isn't so good at optimizing a single one.
Let's read some real world cases:
http://planetcassandra.org/apache-cassandra-use-cases/
In this article: http://planetcassandra.org/blog/post/agentis-energy-stores-over-15-billion-records-of-time-series-usage-data-in-apache-cassandra
They elaborated the reason why they didn't choose MySql is because db synchronization is too slow.
(Also due to 2-phrase commit, FK, PK)
Cassandra is based on Amazon Dynamo paper
Features:
Stability
High availability
Backup performs well
Read and Write is better than HBase, (BigTable clone in java).
wiki http://en.wikipedia.org/wiki/Apache_Cassandra
Their Conclusion is:
We looked at HBase, Dynamo, Mongo and Cassandra.
Cassandra was simply the best storage solution for the majority of our data.
As of 2018,
I would recommend using ScyllaDB to replace classic cassandra, if you need back support.
Postgres kv plugin is also quick than cassandra. How ever won't have multi-instance scalability.
another situation that makes the choice easier is when you want to use aggregate function like sum, min, max, etcetera and complex queries (like in the financial system mentioned above) then a relational database is probably more convenient then a nosql database since both are not possible on a nosql databse unless you use really a lot of Inverted indexes. When you do use nosql you would have to do the aggregate functions in code or store them seperatly in its own columnfamily but this makes it all quite complex and reduces the performance that you gained by using nosql.
Cassandra is a good choice if:
You don't require the ACID properties from your DB.
There would be massive and huge number of writes on the DB.
There is a requirement to integrate with Big Data, Hadoop, Hive and Spark.
There is a need of real time data analytics and report generations.
There is a requirement of impressive fault tolerant mechanism.
There is a requirement of homogenous system.
There is a requirement of lots of customisation for tuning.
If you need a fully consistent database with SQL semantics, Cassandra is NOT the solution for you. Cassandra supports key-value lookups. It does not support SQL queries. Data in Cassandra is "eventually consistent". Concurrent lookups of data may be inconsistent, but eventually lookups are consistent.
If you need strict semantics and need support for SQL queries, choose another solution such as MySQL, PostGres, or combine use of Cassandra with Solr.
Apache cassandra is a distributed database for managing large amounts of structured data across many commodity servers, while providing highly available service and no single point of failure.
The archichecture is purely based on the cap theorem, which is availability , and partition tolerance, and interestingly eventual consistently.
Dont Use it, if your not storing volumes of data across racks of clusters,
Dont use if you are not storing Time series data,
Dont Use if you not patitioning your servers,
Dont use if you require strong Consistency.
Mongodb has very powerful aggregate functions and an expressive aggregate framework. It has many of the features developers are accustomed to using from the relational database world. It's document data/storage structure allows for more complex data models than Cassandra, for example.
All this comes with trade-offs of course. So when you select your database (NoSQL, NewSQL, or RDBMS) look at what problem you are trying to solve and at your scalability needs. No one database does it all.
According to DataStax, Cassandra is not the best use case when there is a need for
1- High end hardware devices.
2- ACID compliant with no roll back (bank transaction)
It does not support complete transaction management across the
tables.
Secondary Index not supported.
Have to rely on Elastic search /Solr for Secondary index and the custom sync component has to be written.
Not ACID compliant system.
Query support is limited.
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Is there any NoSQL data store that is ACID compliant?
I'll post this as an answer purely to support the conversation - Tim Mahy , nawroth , and CraigTP have suggested viable databases. CouchDB would be my preferred due to the use of Erlang, but there are others out there.
I'd say ACID does not contradict or negate the concept of NoSQL... While there seems to be a trend following the opinion expressed by dove , I would argue the concepts are distinct.
NoSQL is fundamentally about simple key-value (e.g. Redis) or document-style schema (collected key-value pairs in a "document" model, e.g. MongoDB) as a direct alternative to the explicit schema in classical RDBMSs. It allows the developer to treat things asymmetrically, whereas traditional engines have enforced rigid same-ness across the data model. The reason this is so interesting is because it provides a different way to deal with change, and for larger data sets it provides interesting opportunities to deal with volumes and performance.
ACID provides principles governing how changes are applied to a database. In a very simplified way, it states (my own version):
(A) when you do something to change a database the change should work or fail as a whole
(C) the database should remain consistent (this is a pretty broad topic)
(I) if other things are going on at the same time they shouldn't be able to see things mid-update
(D) if the system blows up (hardware or software) the database needs to be able to pick itself back up; and if it says it finished applying an update, it needs to be certain
The conversation gets a little more excitable when it comes to the idea of propagation and constraints. Some RDBMS engines provide the ability to enforce constraints (e.g. foreign keys) which may have propagation elements (a la cascade). In simpler terms, one "thing" may have a relationship with another "thing" in the database, and if you change an attribute of one it may require the other be changed (updated, deleted, ... lots of options). NoSQL databases, being predominantly (at the moment) focused on high data volumes and high traffic, seem to be tackling the idea of distributed updates which take place within (from a consumer perspective) arbitrary time frames. This is basically a specialized form of replication managed via transaction - so I would say that if a traditional distributed database can support ACID, so can a NoSQL database.
Some resources for further reading:
Wikipedia article on ACID
Wikipedia on propagation constraints
Wikipedia (yeah, I like the site, ok?) on database normalization
Apache documentation on CouchDB with a good overview of how it applies ACID
Wikipedia on Cluster Computing
Wikipedia (again...) on database transactions
UPDATE (27 July 2012):
Link to Wikipedia article has been updated to reflect the version of the article that was current when this answer was posted. Please note that the current Wikipedia article has been extensively revised!
Well, according to an older version of a Wikipedia article on NoSQL:
NoSQL is a movement promoting a
loosely defined class of
non-relational data stores that break
with a long history of relational
databases and ACID guarantees.
and also:
The name was an attempt to describe
the emergence of a growing number of
non-relational, distributed data
stores that often did not attempt to
provide ACID guarantees.
and
NoSQL systems often provide weak
consistency guarantees such as
eventual consistency and transactions
restricted to single data items, even
though one can impose full ACID
guarantees by adding a supplementary
middleware layer.
So, in a nutshell, I'd say that one of the main benefits of a "NoSQL" data store is its distinct lack of ACID properties. Furthermore, IMHO, the more one tries to implement and enforce ACID properties, the further away from the "spirit" of a "NoSQL" data store you get, and the closer to a "true" RDBMS you get (relatively speaking, of course).
However, all that said, "NoSQL" is a very vague term and is open to individual interpretations, and depends heavily upon just how much of a purist viewpoint you have. For example, most modern-day RDBMS systems don't actually adhere to all of Edgar F. Codd's 12 rules of his relation model!
Taking a pragmatic approach, it would appear that Apache's CouchDB comes closest to embodying both ACID-compliance whilst retaining loosely-coupled, non-relational "NoSQL" mentality.
Please ensure you read the Martin Fowler introduction about NoSQL databases. And the corresponding video.
First of all, we can distinguish two types of NoSQL databases:
Aggregate-oriented databases;
Graph-oriented databases (e.g. Neo4J).
By design, most Graph-oriented databases are ACID!
Then, what about the other types?
In Aggregate-oriented databases, we can put three sub-types:
Document-based NoSQL databases (e.g. MongoDB, CouchDB);
Key/Value NoSQL databases (e.g. Redis);
Column family NoSQL databases (e.g. Hibase, Cassandra).
What we call an Aggregate here, is what Eric Evans defined in its Domain-Driven Design as a self-sufficient of Entities and Value-Objects in a given Bounded Context.
As a consequence, an aggregate is a collection of data that we
interact with as a unit. Aggregates form the boundaries for ACID
operations with the database. (Martin Fowler)
So, at Aggregate level, we can say that most NoSQL databases can be as safe as ACID RDBMS, with the proper settings. Of source, if you tune your server for the best speed, you may come into something non ACID. But replication will help.
My main point is that you have to use NoSQL databases as they are, not as a (cheap) alternative to RDBMS. I have seen too much projects abusing of relations between documents. This can't be ACID. If you stay at document level, i.e. at Aggregate boundaries, you do not need any transaction. And your data will be as safe as with an ACID database, even if it not truly ACID, since you do not need those transactions! If you need transactions and update several "documents" at once, you are not in the NoSQL world any more - so use a RDBMS engine instead!
some 2019 update: Starting in version 4.0, for situations that require atomicity for updates to multiple documents or consistency between reads to multiple documents, MongoDB provides multi-document transactions for replica sets.
In this question someone must mention OrientDB:
OrientDB is a NoSQL database, one of the few, that support fully ACID transactions. ACID is not only for RDBMS because it's not part of the Relational algebra. So it IS possible to have a NoSQL database that support ACID.
This feature is the one I miss the most in MongoDB
FoundationDB is ACID compliant:
http://www.foundationdb.com/
It has proper transactions, so you can update multiple disparate data items in an ACID fashion. This is used as the foundation for maintaining indexes at a higher layer.
ACID and NoSQL are completely orthogonal. One does not imply the other.
I have a notebook on my desk, I use it to keep notes on things that I still have to do. This notebook is a NoSQL database. I query it using a linear search with a "page cache" so I don't always have to search every page. It is also ACID compliant as I ensure that I only write one thing at a time and never while I am reading it.
NoSQL simply means that it isn't SQL. Many people get confused and think it means highly-scaleable-wild-west-super-fast-storage. It doesn't. It doesn't mean key-value store, or eventual consistency. All it means is "not SQL", there are a lot of databases in this planet and most of them are not SQL[citation needed].
You can find many examples in the other answers so I need not list them here, but there are non-SQL databases with ACID compliance for various operations, some are only ACID for single object writes while some guarantee far more. Each database is different.
"NoSQL" is not a well-defined term. It's a very vague concept. As such, it's not even possible to say what is and what is not a "NoSQL" product. Not nearly all of the products typcially branded with the label are key-value stores.
As one of the originators of NoSQL (I was an early contributor to Apache CouchDB, and a speaker at the first NoSQL event held at CBS Interactive / CNET in 2009) I'm excited to see new algorithms create possibilities that didn't exist before. The Calvin protocol offers a new way to think of physical constraints like CAP and PACELC.
Instead of active/passive async replication, or active/active synchronous replication, Calvin preserves correctness and availability during replica outages by using a RAFT-like protocol to maintain a transaction log. Additionally, transactions are processed deterministically at each replica, removing the potential for deadlocks, so agreement is achieved with only a single round of consensus. This makes it fast even on multi-cloud worldwide deployments.
FaunaDB is the only database implementation using the Calvin protocol, making it uniquely suited for workloads that require mainframe-like data integrity with NoSQL scale and flexibility.
Yes, MarkLogic Server is a NoSQL solution (document database I like to call it) that works with ACID transactions
The grandfather of NoSQL: ZODB is ACID compliant. http://www.zodb.org/
However, it's Python only.
If you are looking for an ACID compliant key/value store, there's Berkeley DB. Among graph databases at least Neo4j and HyperGraphDB offer ACID transactions (HyperGraphDB actually uses Berkeley DB for low-level storage at the moment).
FoundationDB was mentioned and at the time it wasn't open source. It's been open sourced by Apple two days ago:
https://www.foundationdb.org/blog/foundationdb-is-open-source/
I believe it is ACID compliant.
MongoDB announced that its 4.0 version will be ACID compliant for multi-document transactions.
Version 4.2. is supposed to support it under sharded setups.
https://www.mongodb.com/blog/post/multi-document-transactions-in-mongodb
NewSQL
This concept Wikipedia contributors define as:
[…] a class of modern relational database management systems that seek to provide the same scalable performance of NoSQL systems for online transaction processing (OLTP) read-write workloads while still maintaining the ACID guarantees of a traditional database system.[1][2][3]
References
[1] Nancy Lynch and Seth Gilbert, “Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services”, ACM SIGACT News, Volume 33 Issue 2 (2002), pg. 51-59.
[2] "Brewer's CAP Theorem", julianbrowne.com, Retrieved 02-Mar-2010
[3] "Brewers CAP theorem on distributed systems", royans.net
take a look at the CAP theorem
EDIT: RavenDB seems to be ACID compliant
To add to the list of alternatives, another fully ACID compliant NoSQL database is GT.M.
Hyperdex Warp http://hyperdex.org/warp/
Warp (ACID feature) is proprietary, but Hyperdex is free.
db4o
Unlike roll-your-own persistence or
serialization, db4o is ACID
transaction safe and allows for
querying, replication and schema
changes during runtime
http://www.db4o.com/about/productinformation/db4o/
BergDB is a light-weight, open-source, NoSQL database designed from the start to run ACID transactions. Actually, BergDB is "more" ACID than most SQL databases in the sense that the only way to change the state of the database is to run ACID transactions with the highest isolation level (SQL term: "serializable"). There will never be any issues with dirty reads, non-repeatable reads, or phantom reads.
In my opinion, the database is still highly performant; but don't trust me, I created the software. Try it yourself instead.
Tarantool is a fully ACID NoSQL database. You can issue CRUD operations or stored procedures, everything will be run with strict accordance with an ACID property. You can also read about that here: http://stable.tarantool.org/doc/mpage/data-and-persistence.html
MarkLogic is also ACID complient. I think is one of the biggest players now.
Wait is over.
ACID compliant NoSQL DB is out ----------- have a look at citrusleaf
A lot of modern NoSQL solution don't support ACID transactions (atomic isolated multi-key updates), but most of them support primitives which allow you to implement transactions on the application level.
If a data store supports per key linearizability and compare-and-set (document level atomicity) then it's enough to implement client-side transactions, more over you have several options to choose from:
If you need Serializable isolation level then you can follow the same algorithm which Google use for the Percolator system or Cockroach Labs for CockroachDB. I've blogged about it and create a step-by-step visualization, I hope it will help you to understand the main idea behind the algorithm.
If you expect high contention but it's fine for you to have Read Committed isolation level then please take a look on the RAMP transactions by Peter Bailis.
The third approach is to use compensating transactions also known as the saga pattern. It was described in the late 80s in the Sagas paper but became more actual with the raise of distributed systems. Please see the Applying the Saga Pattern talk for inspiration.
The list of data stores suitable for client side transactions includes Cassandra with lightweight transactions, Riak with consistent buckets, RethinkDB, ZooKeeper, Etdc, HBase, DynamoDB, MongoDB and others.
YugaByte DB supports an ACID Compliant distributed txns as well as Redis and CQL API compatibility on the query layer.
Google Cloud Datastore is a NoSQL database that supports ACID transactions
DynamoDB is a NoSQL database and has ACID transactions.
VoltDB is an entrant which claims ACID compliance, and while it still uses SQL, its goals are the same in terms of scalability
Whilst it's only an embedded engine and not a server, leveldb has WriteBatch and the ability to turn on Synchronous writes to provide ACID behaviour.
Node levelUP is transactional and built on leveldb https://github.com/rvagg/node-levelup#batch
If you add enough pure water and successfully flip a coin, anything can become acidic. Or basic for that matter.
To say a database is ACID compliant means four specific things. And in defining the system (restricting the range) we can arbitrarily water down the meanings so that the result is ACID compliance.
A—if your NoSQL database only allows one record operation at a time and records either go or they don't then that's atomic.
C—if the only constraints you allow are simple, like checking JSON schemas against a known schema then that's consistent.
I—if just append-only transactions are supported (and schema changes are disallowed) then it is impossible for anything to depend on anything else, that's independent.
D—if you turn off all machines at night and synchronize disks then the transactions will be it in or they won't, that's durable.
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I've been hearing things about NoSQL and that it may eventually become the replacement for SQL DB storage methods due to the fact that DB interaction is often a bottle neck for speed on the web.
So I just have a few questions:
What exactly is it?
How does it work?
Why would it be better than using a SQL Database? And how much better is it?
Is the technology too new to start implementing yet or is it worth taking a look into?
There is no such thing as NoSQL!
NoSQL is a buzzword.
For decades, when people were talking about databases, they meant relational databases. And when people were talking about relational databases, they meant those you control with Edgar F. Codd's Structured Query Language. Storing data in some other way? Madness! Anything else is just flatfiles.
But in the past few years, people started to question this dogma. People wondered if tables with rows and columns are really the only way to represent data. People started thinking and coding, and came up with many new concepts how data could be organized. And they started to create new database systems designed for these new ways of working with data.
The philosophies of all these databases were different. But one thing all these databases had in common, was that the Structured Query Language was no longer a good fit for using them. So each database replaced SQL with their own query languages. And so the term NoSQL was born, as a label for all database technologies which defy the classic relational database model.
So what do NoSQL databases have in common?
Actually, not much.
You often hear phrases like:
NoSQL is scalable!
NoSQL is for BigData!
NoSQL violates ACID!
NoSQL is a glorified key/value store!
Is that true? Well, some of these statements might be true for some databases commonly called NoSQL, but every single one is also false for at least one other. Actually, the only thing NoSQL databases have in common, is that they are databases which do not use SQL. That's it. The only thing that defines them is what sets them apart from each other.
So what sets NoSQL databases apart?
So we made clear that all those databases commonly referred to as NoSQL are too different to evaluate them together. Each of them needs to be evaluated separately to decide if they are a good fit to solve a specific problem. But where do we begin? Thankfully, NoSQL databases can be grouped into certain categories, which are suitable for different use-cases:
Document-oriented
Examples: MongoDB, CouchDB
Strengths: Heterogenous data, working object-oriented, agile development
Their advantage is that they do not require a consistent data structure. They are useful when your requirements and thus your database layout changes constantly, or when you are dealing with datasets which belong together but still look very differently. When you have a lot of tables with two columns called "key" and "value", then these might be worth looking into.
Graph databases
Examples: Neo4j, GiraffeDB.
Strengths: Data Mining
While most NoSQL databases abandon the concept of managing data relations, these databases embrace it even more than those so-called relational databases.
Their focus is at defining data by its relation to other data. When you have a lot of tables with primary keys which are the primary keys of two other tables (and maybe some data describing the relation between them), then these might be something for you.
Key-Value Stores
Examples: Redis, Cassandra, MemcacheDB
Strengths: Fast lookup of values by known keys
They are very simplistic, but that makes them fast and easy to use. When you have no need for stored procedures, constraints, triggers and all those advanced database features and you just want fast storage and retrieval of your data, then those are for you.
Unfortunately they assume that you know exactly what you are looking for. You need the profile of User157641? No problem, will only take microseconds. But what when you want the names of all users who are aged between 16 and 24, have "waffles" as their favorite food and logged in in the last 24 hours? Tough luck. When you don't have a definite and unique key for a specific result, you can't get it out of your K-V store that easily.
Is SQL obsolete?
Some NoSQL proponents claim that their favorite NoSQL database is the new way of doing things, and SQL is a thing of the past.
Are they right?
No, of course they aren't. While there are problems SQL isn't suitable for, it still got its strengths. Lots of data models are simply best represented as a collection of tables which reference each other. Especially because most database programmers were trained for decades to think of data in a relational way, and trying to press this mindset onto a new technology which wasn't made for it rarely ends well.
NoSQL databases aren't a replacement for SQL - they are an alternative.
Most software ecosystems around the different NoSQL databases aren't as mature yet. While there are advances, you still haven't got supplemental tools which are as mature and powerful as those available for popular SQL databases.
Also, there is much more know-how for SQL around. Generations of computer scientists have spent decades of their careers into research focusing on relational databases, and it shows: The literature written about SQL databases and relational data modelling, both practical and theoretical, could fill multiple libraries full of books. How to build a relational database for your data is a topic so well-researched it's hard to find a corner case where there isn't a generally accepted by-the-book best practice.
Most NoSQL databases, on the other hand, are still in their infancy. We are still figuring out the best way to use them.
What exactly is it?
On one hand, a specific system, but it has also become a generic word for a variety of new data storage backends that do not follow the relational DB model.
How does it work?
Each of the systems labelled with the generic name works differently, but the basic idea is to offer better scalability and performance by using DB models that don't support all the functionality of a generic RDBMS, but still enough functionality to be useful. In a way it's like MySQL, which at one time lacked support for transactions but, exactly because of that, managed to outperform other DB systems. If you could write your app in a way that didn't require transactions, it was great.
Why would it be better than using a SQL Database? And how much better is it?
It would be better when your site needs to scale so massively that the best RDBMS running on the best hardware you can afford and optimized as much as possible simply can't keep up with the load. How much better it is depends on the specific use case (lots of update activity combined with lots of joins is very hard on "traditional" RDBMSs) - could well be a factor of 1000 in extreme cases.
Is the technology too new to start implementing yet or is it worth taking a look into?
Depends mainly on what you're trying to achieve. It's certainly mature enough to use. But few applications really need to scale that massively. For most, a traditional RDBMS is sufficient. However, with internet usage becoming more ubiquitous all the time, it's quite likely that applications that do will become more common (though probably not dominant).
Since someone said that my previous post was off-topic, I'll try to compensate :-) NoSQL is not, and never was, intended to be a replacement for more mainstream SQL databases, but a couple of words are in order to get things in the right perspective.
At the very heart of the NoSQL philosophy lies the consideration that, possibly for commercial and portability reasons, SQL engines tend to disregard the tremendous power of the UNIX operating system and its derivatives.
With a filesystem-based database, you can take immediate advantage of the ever-increasing capabilities and power of the underlying operating system, which have been steadily increasing for many years now in accordance with Moore's law. With this approach, many operating-system commands become automatically also "database operators" (think of "ls" "sort", "find" and the other countless UNIX shell utilities).
With this in mind, and a bit of creativity, you can indeed devise a filesystem-based database that is able to overcome the limitations of many common SQL engines, at least for specific usage patterns, which is the whole point behind NoSQL's philosophy, the way I see it.
I run hundreds of web sites and they all use NoSQL to a greater or lesser extent. In fact, they do not host huge amounts of data, but even if some of them did I could probably think of a creative use of NoSQL and the filesystem to overcome any bottlenecks. Something that would likely be more difficult with traditional SQL "jails". I urge you to google for "unix", "manis" and "shaffer" to understand what I mean.
If I recall correctly, it refers to types of databases that don't necessarily follow the relational form. Document databases come to mind, databases without a specific structure, and which don't use SQL as a specific query language.
It's generally better suited to web applications that rely on performance of the database, and don't need more advanced features of Relation Database Engines. For example, a Key->Value store providing a simple query by id interface might be 10-100x faster than the corresponding SQL server implementation, with a lower developer maintenance cost.
One example is this paper for an OLTP Tuple Store, which sacrificed transactions for single threaded processing (no concurrency problem because no concurrency allowed), and kept all data in memory; achieving 10-100x better performance as compared to a similar RDBMS driven system. Basically, it's moving away from the 'One Size Fits All' view of SQL and database systems.
In practice, NoSQL is a database system which supports fast access to large binary objects (docs, jpgs etc) using a key based access strategy. This is a departure from the traditional SQL access which is only good enough for alphanumeric values. Not only the internal storage and access strategy but also the syntax and limitations on the display format restricts the traditional SQL. BLOB implementations of traditional relational databases too suffer from these restrictions.
Behind the scene it is an indirect admission of the failure of the SQL model to support any form of OLTP or support for new dataformats. "Support" means not just store but full access capabilities - programmatic and querywise using the standard model.
Relational enthusiasts were quick to modify the defnition of NoSQL from Not-SQL to Not-Only-SQL to keep SQL still in the picture! This is not good especially when we see that most Java programs today resort to ORM mapping of the underlying relational model. A new concept must have a clearcut definition. Else it will end up like SOA.
The basis of the NoSQL systems lies in the random key - value pair. But this is not new. Traditional database systems like IMS and IDMS did support hashed ramdom keys (without making use of any index) and they still do. In fact IDMS already has a keyword NONSQL where they support SQL access to their older network database which they termed as NONSQL.
It's like Jacuzzi: both a brand and a generic name. It's not just a specific technology, but rather a specific type of technology, in this case referring to large-scale (often sparse) "databases" like Google's BigTable or CouchDB.
NoSQL the actual program appears to be a relational database implemented in awk using flat files on the backend. Though they profess, "NoSQL essentially has no arbitrary limits, and can work where other products can't. For example there is no limit on data field size, the number of columns, or file size" , I don't think it is the large scale database of the future.
As Joel says, massively scalable databases like BigTable or HBase, are much more interesting. GQL is the query language associated with BigTable and App Engine. It's largely SQL tweaked to avoid features Google considers bottle-necks (like joins). However, I haven't heard this referred to as "NoSQL" before.
NoSQL is a database system which doesn't use string based SQL queries to fetch data.
Instead you build queries using an API they will provide, for example Amazon DynamoDB is a good example of a NoSQL database.
NoSQL databases are better for large applications where scalability is important.
Does NoSQL mean non-relational database?
Yes, NoSQL is different from RDBMS and OLAP. It uses looser consistency models than traditional relational databases.
Consistency models are used in distributed systems like distributed shared memory systems or distributed data store.
How it works internally?
NoSQL database systems are often highly optimized for retrieval and appending operations and often offer little functionality beyond record storage (e.g. key-value stores). The reduced run-time flexibility compared to full SQL systems is compensated by marked gains in scalability and performance for certain data models.
It can work on Structured and Unstructured Data. It uses Collections instead of Tables
How do you query such "database"?
Watch SQL vs NoSQL: Battle of the Backends; it explains it all.