Related
As mentioned in the following article : http://www.couchbase.com/why-nosql/nosql-database
When looking up data, the desired information needs to be collected from many tables (often hundreds in today’s enterprise applications) and combined before it can be provided to the application. Similarly, when writing data, the write needs to be coordinated and performed on many tables.
and the given example of data in JSON format tells
ease of efficiently distributing the resulting documents and read and write performance improvements make it an easy trade-off for web-based applications
But what if i capture all my data in a single table in mysql as is done in mongoDB [in the link given] , would that performance be like equivalent to mongoDB [meaning extracting data from mysql without JOINS] ?
It all depends on the structure you require. The main point of splitting data into tables is being able to index pieces of data, accelerating the retrieval of data.
Another point is that the normalization that a relational database offers ties you to a rigid structure. You can, of course, store json in mysql, but the json document won't have its pieces indexed. If you want fast retrieval of a json document by its pieces then you are looking into splitting it into parts.
If your data can change, which means, doesn't require a schema, then use Mongo.
If your data structure doesn't change then I'd go with MySQL
I realize that this question is pretty well discussed, however I would like to get your input in the context of my specific needs.
I am developing a realtime financial database that grabs stock quotes from the net multiple times a minute and stores it in a database. I am currently working with SQLAlchemy over MySQL, but I came across Redis and it looks interesting. It looks good especially because of its performance, which is crucial in my application. I know that MySQL can be fast too, I just feel like implementing heavy caching is going to be a pain.
The data I am saving is by far mostly decimal values. I am also doing a significant amount of divisions and multiplications with these decimal values (in a different application).
In terms of data size, I am grabbing about 10,000 symbols multiple times a minute. This amounts to about 3 TB of data a year.
I am also concerned by Redis's key quantity limitation (2^32). Is Redis a good solution here? What other factors can help me make the decision either toward MySQL or Redis?
Thank you!
Redis is an in-memory store. All the data must fit in memory. So except if you have 3 TB of RAM per year of data, it is not the right option. The 2^32 limit is not really an issue in practice, because you would probably have to shard your data anyway (i.e. use multiple instances), and because the limit is actually 2^32 keys with 2^32 items per key.
If you have enough memory and still want to use (sharded) Redis, here is how you can store space efficient time series: https://github.com/antirez/redis-timeseries
You may also want to patch Redis in order to add a proper time series data structure. See Luca Sbardella's implementation at:
https://github.com/lsbardel/redis
http://lsbardel.github.com/python-stdnet/contrib/redis_timeseries.html
Redis is excellent to aggregate statistics in real time and store the result of these caclulations (i.e. DIRT applications). However, storing historical data in Redis is much less interesting, since it offers no query language to perform offline calculations on these data. Btree based stores supporting sharding (MongoDB for instance) are probably more convenient than Redis to store large time series.
Traditional relational databases are not so bad to store time series. People have dedicated entire books to this topic:
Developing Time-Oriented Database Applications in SQL
Another option you may want to consider is using a bigdata solution:
storing massive ordered time series data in bigtable derivatives
IMO the main point (whatever the storage engine) is to evaluate the access patterns to these data. What do you want to use these data for? How will you access these data once they have been stored? Do you need to retrieve all the data related to a given symbol? Do you need to retrieve the evolution of several symbols in a given time range? Do you need to correlate values of different symbols by time? etc ...
My advice is to try to list all these access patterns. The choice of a given storage mechanism will only be a consequence of this analysis.
Regarding MySQL usage, I would definitely consider table partitioning because of the volume of the data. Depending on the access patterns, I would also consider the ARCHIVE engine. This engine stores data in compressed flat files. It is space efficient. It can be used with partitioning, so despite it does not index the data, it can be efficient at retrieving a subset of data if the partition granularity is carefully chosen.
You should consider Cassandra or Hbase. Both allow contiguous storage and fast appends, so that when it comes to querying, you get huge performance. Both will easily ingest tens of thousands of points per second.
The key point is along one of your query dimensions (usually by ticker), you're accessing disk (ssd or spinning), contiguously. You're not having to hit indices millions of times. You can model things in Mongo/SQL to get similar performance, but it's more hassle, and you get it "for free" out of the box with the columnar guys, without having to do any client side shenanigans to merge blobs together.
My experience with Cassandra is that it's 10x faster than MongoDB, which is already much faster than most relational databases, for the time series use case, and as data size grows, its advantage over the others grows too. That's true even on a single machine. Here is where you should start.
The only negative on Cassandra at least is that you don't have consistency for a few seconds sometimes if you have a big cluster, so you need either to force it, slowing it down, or you accept that the very very latest print sometimes will be a few seconds old. On a single machine there will be zero consistency problems, and you'll get the same columnar benefits.
Less familiar with Hbase but it claims to be more consistent (there will be a cost elsewhere - CAP theorem), but it's much more of a commitment to setup the Hbase stack.
You should first check the features that Redis offers in terms of data selection and aggregation. Compared to an SQL database, Redis is limited.
In fact, 'Redis vs MySQL' is usually not the right question, since they are apples and pears. If you are refreshing the data in your database (also removing regularly), check out MySQL partitioning. See e.g. the answer I wrote to What is the best way to delete old rows from MySQL on a rolling basis?
>
Check out MySQL Partitioning:
Data that loses its usefulness can often be easily removed from a partitioned table by dropping the partition (or partitions) containing only that data. Conversely, the process of adding new data can in some cases be greatly facilitated by adding one or more new partitions for storing specifically that data.
See e.g. this post to get some ideas on how to apply it:
Using Partitioning and Event Scheduler to Prune Archive Tables
And this one:
Partitioning by dates: the quick how-to
I'm new to databases, and this question has to do with how smart I can expect databases to be. Here by "databases" I mean "something like" MySQL or H2 (I actually have no idea if these two are similar, just that they are popular). I'm actually using ScalaQuery, so it abstracts away from the underlying database.
Suppose I have a table with entries of type (String, Int), with lots of redundancy in the String entries. So my table might look like:
(Adam, 18)
(Adam, 24)
(Adam, 34)
... continued ...
(Adam, 3492)
(Bethany, 4)
(Bethany, 45)
... continued ...
(Bethany, 2842)
If I store this table with H2, is it going to be smart enough to realize "Adam" and "Bethany" are repeated lots of times, and can be replaced with enumerations pointing to lookup tables? Or is it going to waste lots of storage?
Related: If H2 is smart in this respect with strings, is it also smart in the same way with doubles? In my probably brain-dead initial table, I happen to have lots of repeated double fields.
Thanks!
The database engine is not built to recognize redundancies in data and fix them. That is the task of the designer / developer.
Databases are designed to store information. There is no way database will know if (Adam, 44) and (Adam,55) can be compressed, and I would be petrified if databases tried to do things like you propose, as this can lead to a various performance and/or logical problems.
On the opposite, databases are not minimising the storage, they are adding redundant information, like indexes and keys, and other internal additional information required for DB.
DBs are built to retrieve information fast, not store it space-effectively. When it comes to complexity, database rather increase storage space, then decrease the performance of a query.
There are some storage systems that compress pages, so the question is valid. I can't talk about MySQL, but I believe it is similar to H2. H2 isn't very smart in this regard. H2 does compress data, but only for the following cases:
LOB compression, if enabled.
The following does not effect storage size of a closed database: H2 compresses the undo log when writing using LZF currently, therefore repeated data in a page will result in a slightly improved write performance (but only after a checkpoint). This may change in the future however.
Also, H2 uses a coded similar to UTF-8 to store text, but I wouldn't call this compression.
MySQL and other SQL products based on contiguous storage are not smart at this kind of thing at all.
Consider two logical sets, one referencing the other (i.e. a foreign key). One possible implementation is to physically store the value common to both sets just once and for both tables to store a pointer to the value (think reference type variables in 3GL programming languages such as C#). However, most SQL products physically store the value in both tables; if you want pointers then the end user has to implement them themselves, typically using autoincrement integer 'surrogate' keys, which sadly get exposed into the logical model.
Either you are talking about data compression, which can be done by the database engine and shouldn't be your concern.
Or you are talking about data normalization. Then you should read up on database design.
Databases are meant to store data, so no need to worry about a bit of redundancy. If you are going into several million lines and gigabytes of data, then you can start considering options. But up to that level you will not have any problems with performance.
I'm trying to implement a key/value store with mysql
I have a user table that has 2 columns, one for the global ID and one for the serialized data.
Now the problem is that everytime any bit of the user's data changes, I will have to retrieve the serialized data from the db, alter the data, then reserialize it and throw it back into the db. I have to repeat these steps even if there is a very very small change to any of the user's data (since there's no way to update that cell within the db itself)
Basically i'm looking at what solutions people normally use when faced with this problem?
Maybe you should preprocess your JSON data and insert data as a proper MySQL row separated into fields.
Since your input is JSON, you have various alternatives for converting data:
You mentioned many small changes happen in your case. Where do they occur? Do they happen in a member of a list? A top-level attribute?
If updates occur mainly in list members in a part of your JSON data, then perhaps every member should in fact be represented in a different table as separate rows.
If updates occur in an attribute, then represent it as a field.
I think cost of preprocessing won't hurt in your case.
When this is a problem, people do not use key/value stores, they design a normalized relational database schema to store the data in separate, single-valued columns which can be updated.
To be honest, your solution is using a database as a glorified file system - I would not recommend this approach for application data that is core to your application.
The best way to use a relational database, in my opinion, is to store relational data - tables, columns, primary and foreign keys, data types. There are situations where this doesn't work - for instance, if your data is really a document, or when the data structures aren't known in advance. For those situations, you can either extend the relational model, or migrate to a document or object database.
In your case, I'd see firstly if the serialized data could be modeled as relational data, and whether you even need a database. If so, move to a relational model. If you need a database but can't model the data as a relational set, you could go for a key/value model where you extract your serialized data into individual key/value pairs; this at least means that you can update/add the individual data field, rather than modify the entire document. Key/value is not a natural fit for RDBMSes, but it may be a smaller jump from your current architecture.
when you have a key/value store, assuming your serialized data is JSON,it is effective only when you have memcached along with it, because you don't update the database on the fly every time but instead you update the memcache & then push that to your database in background. so definitely you have to update the entire value but not an individual field in your JSON data like address alone in database. You can update & retrieve data fast from memcached. since there are no complex relations in database it will be fast to push & pull data from database to memcache.
I would continue with what you are doing and create separate tables for the indexable data. This allows you to treat your database as a single data store which is managed easily through most operation groups including updates, backups, restores, clustering, etc.
The only thing you may want to consider is to add ElasticSearch to the mix if you need to perform anything like a like query just for improved search performance.
If space is not an issue for you, I would even make it an insert only database so any changes adds a new record that way you can keep the history. Of course you may want to remove the older records but you can have a background job that would delete the superseded records in a batch in the background. (Mind you what I described is basically Kafka)
There's many alternatives out there now that beats RDBMS in terms of performance. However, they all add extra operational overhead in that it's yet another middleware to maintain.
The way around that if you have a microservices architecture is to keep the middleware as part of your microservice stack. However, you have to deal with transmitting the data across the microservices so you'd still end up with a switch to Kafka underneath it all.
MySQL, PostgreSQL and MS SQL Server are relational database systems, and NoSQL, MongoDB, etc. are non-relational DBMSs.
What are the differences between the two types of system?
Hmm, not quite sure what your question is.
In the title you ask about Databases (DB), whereas in the body of your text you ask about Database Management Systems (DBMS). The two are completely different and require different answers.
A DBMS is a tool that allows you to access a DB.
Other than the data itself, a DB is the concept of how that data is structured.
So just like you can program with Oriented Object methodology with a non-OO powered compiler, or vice-versa, so can you set-up a relational database without an RDBMS or use an RDBMS to store non-relational data.
I'll focus on what Relational Database (RDB) means and leave the discussion about what systems do to others.
A relational database (the concept) is a data structure that allows you to link information from different 'tables', or different types of data buckets. A data bucket must contain what is called a key or index (that allows to uniquely identify any atomic chunk of data within the bucket). Other data buckets may refer to that key so as to create a link between their data atoms and the atom pointed to by the key.
A non-relational database just stores data without explicit and structured mechanisms to link data from different buckets to one another.
As to implementing such a scheme, if you have a paper file with an index and in a different paper file you refer to the index to get at the relevant information, then you have implemented a relational database, albeit quite a simple one. So you see that you do not even need a computer (of course it can become tedious very quickly without one to help), similarly you do not need an RDBMS, though arguably an RDBMS is the right tool for the job. That said there are variations as to what the different tools out there can do so choosing the right tool for the job may not be all that straightforward.
I hope this is layman terms enough and is helpful to your understanding.
Relational databases have a mathematical basis (set theory, relational theory), which are distilled into SQL == Structured Query Language.
NoSQL's many forms (e.g. document-based, graph-based, object-based, key-value store, etc.) may or may not be based on a single underpinning mathematical theory. As S. Lott has correctly pointed out, hierarchical data stores do indeed have a mathematical basis. The same might be said for graph databases.
I'm not aware of a universal query language for NoSQL databases.
Most of what you "know" is wrong.
First of all, as a few of the relational gurus routinely (and sometimes stridently) point out, SQL doesn't really fit nearly as closely with relational theory as many people think. Second, most of the differences in "NoSQL" stuff has relatively little to do with whether it's relational or not. Finally, it's pretty difficult to say how "NoSQL" differs from SQL because both represent a pretty wide range of possibilities.
The one major difference that you can count on is that almost anything that supports SQL supports things like triggers in the database itself -- i.e. you can design rules into the database proper that are intended to ensure that the data is always internally consistent. For example, you can set things up so your database asserts that a person must have an address. If you do so, anytime you add a person, it will basically force you to associate that person with some address. You might add a new address or you might associate them with some existing address, but one way or another, the person must have an address. Likewise, if you delete an address, it'll force you to either remove all the people currently at that address, or associate each with some other address. You can do the same for other relationships, such as saying every person must have a mother, every office must have a phone number, etc.
Note that these sorts of things are also guaranteed to happen atomically, so if somebody else looks at the database as you're adding the person, they'll either not see the person at all, or else they'll see the person with the address (or the mother, etc.)
Most of the NoSQL databases do not attempt to provide this kind of enforcement in the database proper. It's up to you, in the code that uses the database, to enforce any relationships necessary for your data. In most cases, it's also possible to see data that's only partially correct, so even if you have a family tree where every person is supposed to be associated with parents, there can be times that whatever constraints you've imposed won't really be enforced. Some will let you do that at will. Others guarantee that it only happens temporarily, though exactly how long it can/will last can be open to question.
The relational database uses a formal system of predicates to address data. The underlying physical implementation is of no substance and can vary to optimize for certain operations, but it must always assume the relational model. In layman's terms, that's just saying I know exactly how many values (attributes) each row (tuple) in my table (relation) has and now I want to exploit the fact accordingly, thoroughly and to it's extreme. That's the true nature of the beast.
Since we're obviously the generation that has had a relational upbringing, if you look at NoSQL database models from the perspective of the relational model, again in layman's terms, the first obvious difference is that no assumptions about the number of values a row can contain is ever made. This is really oversimplifying the matter and does not cleanly apply to the intricacies of the physical models of every NoSQL database, but it's the pinnacle of the relational model and the first assumption we have to leave behind or, if you'd rather, the biggest leap we have to make.
We can agree to two things that are true for every DBMS: it can store any kind of data and has enough mathematical underpinnings to make it possible to manage the data in any way imaginable. The reality is that you'll never want to make the mistake of putting any of the two points to the test, but rather just stick with what the actual DBMS was really made for. In layman's terms: respect the beast within!
(Please note that I've avoided comparing the (obviously) well founded standards revolving around the relational model against the many flavors provided by NoSQL databases. If you'd like, consider NoSQL databases as an umbrella term for any DBMS that does not completely assume the relational model, in exclusion to everything else. The differences are too many, but that's the principal difference and the one I think would be of most use to you to understand the two.)
Try to explain this question in a level referring to a little bit technology
Take MongoDB and Traditional SQL for comparison, imagine the scenario of posting a Tweet on Twitter. This tweet contains 9 pictures. How do you store this tweet and its corresponding pictures?
In terms of traditional relationship SQL, you can store the tweets and pictures in separate tables, and represent the connection through building a new table.
What's more, you can set a field which is an image type, and zip the 9 pictures into a binary document and store it in this field.
Using MongoDB, you could build a document like this (similar to the concept of a table in relational SQL):
{
"id":"XXX",
"user":"XXX",
"date":"xxxx-xx-xx",
"content":{
"text":"XXXX",
"picture":["p1.png","p2.png","p3.png"]
}
Therefore, in my opinion, the main difference is about how do you store the data and the storage level of the relationships between them.
In this example, the data is the tweet and the pictures. The different mechanism about storage level of relationship between them also play a important role in the difference between both.
I hope this small example helps show the difference between SQL and NoSQL (ACID and BASE).
Here's a link of picture about the goals of NoSQL from the Internet:
http://icamchuwordpress-wordpress.stor.sinaapp.com/uploads/2015/01/dbc795f6f262e9d01fa0ab9b323b2dd1_b.png
The difference between relational and non-relational is exactly that. The relational database architecture provides with constraints objects such as primary keys, foreign keys, etc that allows one to tie two or more tables in a relation. This is good so that we normalize our tables which is to say split information about what the database represents into many different tables, once can keep the integrity of the data.
For example, say you have a series of table that houses information about an employee. You could not delete a record from a table without deleting all the records that pertain to such record from the other tables. In this way you implement data integrity. The non-relational database doesn't provide this constraints constructs that will allow you to implement data integrity.
Unless you don't implement this constraint in the front end application that is utilized to populate the databases' tables, you are implementing a mess that can be compared with the wild west.
First up let me start by saying why we need a database.
We need a database to help organise information in such a manner that we can retrieve that data stored in a efficient manner.
Examples of relational database management systems(SQL):
1)Oracle Database
2)SQLite
3)PostgreSQL
4)MySQL
5)Microsoft SQL Server
6)IBM DB2
Examples of non relational database management systems(NoSQL)
1)MongoDB
2)Cassandra
3)Redis
4)Couchbase
5)HBase
6)DocumentDB
7)Neo4j
Relational databases have normalized data, as in information is stored in tables in forms of rows and columns, and normally when data is in normalized form, it helps to reduce data redundancy, and the data in tables are normally related to each other, so when we want to retrieve the data, we can query the data by using join statements and retrieve data as per our need.This is suited when we want to have more writes, less reads, and not much data involved, also its really easy relatively to update data in tables than in non relational databases. Horizontal scaling not possible, vertical scaling possible to some extent.CAP(Consistency, Availability, Partition Tolerant), and ACID (Atomicity, Consistency, Isolation, Duration)compliance.
Let me show entering data to a relational database using PostgreSQL as an example.
First create a product table as follows:
CREATE TABLE products (
product_no integer,
name text,
price numeric
);
then insert the data
INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99);
Let's look at another different example:
Here in a relational database, we can link the student table and subject table using relationships, via foreign key, subject ID, but in a non relational database no need to have two documents, as no relationships, so we store all the subject details and student details in one document say student document, then data is getting duplicated, which makes updating records troublesome.
In non relational databases, there is no fixed schema, data is not normalized. no relationships between data is created, all data mostly put in one document. Well suited when handling lots of data, and can transfer lots of data at once, best where high amounts of reads and less writes, and less updates, bit difficult to query data, as no fixed schema. Horizontal and vertical scaling is possible.CAP (Consistency, Availability, Partition Tolerant)and BASE (Basically Available, soft state, Eventually consistent)compliance.
Let me show an example to enter data to a non relational database using Mongodb
db.users.insertOne({name: ‘Mary’, age: 28 , occupation: ‘writer’ })
db.users.insertOne({name: ‘Ben’ , age: 21})
Hence you can understand that to the database called db, and there is a collections called users, and document called insertOne to which we add data, and there is no fixed schema as our first record has 3 attributes, and second attribute has 2 attributes only, this is no problem in non relational databases, but this cannot be done in relational databases, as relational databases have a fixed schema.
Let's look at another different example
({Studname: ‘Ash’, Subname: ‘Mathematics’, LecturerName: ‘Mr. Oak’})
Hence we can see in non relational database we can enter both student details and subject details into one document, as no relationships defined in non relational databases, but here this way can lead to data duplication, and hence errors in updating can occur therefore.
Hope this explains everything
In layman terms it's strongly structured vs unstructured, which implies that you have different degrees of adaptability for your DB.
Differences arise in indexation particularly as you need to ensure that a certain reference index can link to a another item -> this a relation. The more strict structure of relational DB comes from this requirement.
To note that NosDB apaprently provides both relational and non relational DBs and a way to query both http://www.alachisoft.com/nosdb/sql-cheat-sheet.html