I am noob in NoSQL world. but After going thru the basics of how Neo4J works, I didnt unerstand how will replication be fast compared to column or document databases or a plain key value DB.
It has nodes and edges which are nothing but relations between those nodes, something simiar to Joins in a RDBMS.
So how does replication works here as ccompared to an RDBMS ?
Each NoSQL database will behave in different ways when it comes to replication. NoSQL is quite a wide term, so you should not expect to have good replication performance for all of them. In fact Neo4j Enterprise has some support for replication, but the design of Neo4j does not naturally lead to scaling. It is certainly not of of the core objectives, unlike others like Cassandra for example.
What do you mean with replication? Neo4j enterprise comes with an high-availability cluster that replicates your data across a number of machines.
If it is just about replicating the data, you can also shutdown your database and copy the database files (or in enterprise execute a online backup).
Related
I'm getting familiar with the greenplum solution concepts, and trying to understand whether, and if so, when the organisation I work for should use this solution. Our conceptual idea is to setup a kind of central 'datastore' suitable for both OLTP and OLAP access.
My research: this article suggests Greenplum is more suitable for OLAP, and PostgreSQL for OLTP. But I also read about Greenplum improvements for OLTP processing. And in favour of Postgresql, there are also articles like this that suggest that OLAP (eg, a datawarehouse implementation) can be done by means of Postgresql.
So my question is: how to move forward, and what are the main criteria to decide? For example, in case we now have a just a few TB's (1-5), start with a Postgresql cluster (for OLTP+OLAP), and when data volumes grow, move on to Greenplum? Or start straight away with Greenplum?
maybe use postgres if it can handle your use case. If you have you have too much data and need to finish reports and analytics faster; change to greenplum
I have an existing relational Postgresql database. A few of the tables contain very fat blobs, they would be much better of as NoSQL Documents. This would significantly lighten our relational database.
So, we thought of moving those blob-table out into a NoSQL solution like CosmosDB or MongoDB. However there are foreign key dependencies with purely relational tables and this complicates moving those tables out into their own database.
I have found that PSQL natively supports storing Documents and can be distributed. The solutions I looked at so far are CitusData and Postgres XL. For those who used those how do they compare?
Has anyone encountered similar situations before? Did you separate out into a NoSQL database? Or has anyone partitioned their PSQL into relational and NoSQL parts? How did that go? What would you recommend to look out for in hindsight?
(Citus Engineer Here)
Postgres has JSONB column type which is powerful and flexible. What you can do is to keep your structural table as is and put a jsonb column for the blob data. Test this with single node Postgres and if that works for you, great!
If you have a problem with the scale of your data, i.e. memory or storage or CPU of a single machine is not enough for your workload and you cannot go bigger, then you can try scaling out with Citus or Postgres-XL.
I have no experience with Postgres-XL but Citus is pretty easy to try. There are docker images that you can use or you can create an account on Citus Cloud to try a 1-week free dev plan (it would not be suitable for benchmarking purposes).
Every RDBMS->NoSQL migration would require one of the two:
1. embedding some of these dependent documents into the ones that are actually queried by the user
2. referencing dependent documents by id and inferring these relationships on read.
Very typical, everyone does it every day, don't be afraid. BTW, you don't have to make a choice between Cosmos DB and MongoDB - just use Cosmos DB with MongoDB API.
I would like to have a Postgres database which is in sync with my production database like a read-replica, but I would also like to write to that database. AWS provides read-replicas to be writable for MySQL and MariaDB but not Postgres. Is there any other way to achieve this?
Well, by definition, read replicas are not writable, so I'm afraid I don't think you'll have much luck with that approach.
Amazon themselves state that read replicas are for read only traffic:
You can create one or more replicas of a given source DB Instance and
serve high-volume application read traffic from multiple copies of
your data, thereby increasing aggregate read throughput.
Now, as you say, for MySQL read replicas can be promoted to masters (and therefore become writable), but pay special attention to the "when needed" below:
Read replicas can also be promoted when needed to become standalone DB
instances.
However, RDS itself does not support multi-master deployments for MySQL.
For PostgreSQL things are even "worse". AWS RDS for Postgres does not (at the time of writing this) support automatic promotion of read-replicas, leaving you with Multi-AZ as your only option.
Outside RDS, multi-master deployments of PostgreSQL (which sounds like what you're looking for) require an even more elaborate setup. You can find more information in the clustering section of their wiki.
As a general note, horizontally scaling relational / SQL databases is probably not something you'll have a lot of fun with and you're bound to run into problems along the way.
That's because they were simply not designed for horizontal scaling the same way that newer "NoSQL" databases are (take a look at MongoDB or Cassandra, etc.). You are far better off scaling them vertically, for as far as that will take you (and it will take you quite some way).
The only relational database that I know of that's (being) built to scale out is CockroachDB, but albeit a very promising solution, that's still in beta -- there's no 1.0 release of it yet.
I have an application that can not afford to lose data, so Postgresql is my choice for database (ACID)
However, speed and query advantages of MongoDB are very attractive, but based on what I've read so far, MongoDB can report a successful write which may not have gone to disk, so I can't make it my mission critical db (I'll also need transactions)
I've seen references to people using mysql and MongoDB together, one for the transactions and the other for queries. Please not that I'm not talking about keeping some data in one DB and the rest in another. I want to use Postgresql as a gateway to data entry, and MongoDB for reads.
Are there any resources that offer an architecture/guide for Postgresql + MongoDB usage in this way? I can remember seeing this topic in Postgresql conference agenda, but I could not find the link.
I don't think you'll get much speed using MongoDB just as a cache. It's strengths are replication and horizontal scalability. On one computer you'd make Mongo and Postgres compete for memory, IO bandwidth and processor time.
As you can not afford to loose transactions you'll be better with Postgres only. Its has efficient caching, sophisticated query planner, prepared queries and wide indexing support cause that read-only queries will be very fast - really comparable to MongoDB on a single computer.
Postgres can even scale horizontally now using asynchronous, or, from version 9.1, synchronous replication.
One way to achieve this would be to set up a master-slave replication with the PostgreSQL database as master, and the MongoDB database as slave. You would then do all reads from MongoDB, and all writes to PostgreSQL.
This post discusses such a setup using a tool called Bucardo:
http://blog.endpoint.com/2011/06/mongodb-replication-from-postgres-using.html
You may also be able to do it with Tungsten Replicator, although it seems designed to be used with MySQL:
http://code.google.com/p/tungsten-replicator/wiki/TRCHeterogeneousReplication
I can remember seeing this topic in Postgresql conference agenda, but I could not find the
link.
Maybe, you are talking about this: https://www.postgresqlconference.org/content/hybrid-applications-using-mongodb-and-postgres
Depending how important transactions are to you, one option is to use MongoDb driver's safe mode and drop Postgresql.
http://www.mongodb.org/display/DOCS/getLastError+Command
How can you expect transactional consistency from Postgres but trust MongoDB for reads? How would you support rollbacks in this scenario? How do you detect when they've gotten out of sync?
I think you're better off going with memcache and implementing a higher level object cache. Alternatively, you could consider a replication slave for reads. If you have performance needs beyond what a dedicated read slave can provide, consider denormalizing your tables on your slave system.
Make sure that any of this is actually needed. For thin tables with PK lookups most modern database engines like Postgres or InnoDB are going to generally keep up with NoSQL solutions. Don't fall into the ROFLSCALE trap
http://www.youtube.com/watch?v=b2F-DItXtZs
I think you can run a mongo replica set.. Let say 3 Slave and 1 Master.. Then in your app you should run all write transactions on Postgresql and then on Mongo ReplicaSet.. After that you can query read operations on Mongo Replica set..
But Synchronizing will be a problem, you should work on it..
you may find some replacement for mongo in here or here that is safer and fast as well.
but I advise to simplify your solution instead of making a complicated design.
Visual Guide to NoSQL Systems
lucky
In mongodb we can specify writeConcern property to specify that it should write to journal/ instances and then send confirmation/ acknowledgement and i think even mongodb has teh concept of transactions. Not sure why we need postgres behind it.
I want to partition a very large PostgreSQL 8.3 database. Quoting the manual,
Partitioning can provide several
benefits:
...
Seldom-used data can be
migrated to cheaper and slower storage
media.
What's the right way to relocate tables to another media or computer?
Adam
What you are talking about is commonly referred to as Replication or Clustering, depending on how the system is set up.
What you want to do specifically is clustering, and you can do it on PostgreSQL.
The wiki lists some of the existing solutions:
Greenplum Database (formerly Bizgres MPP), proprietary. Not so much a replication solution as a way to parallelize queries, and targeted at the data warehousing crowd. Similar to ExtenDB, but tightly integrated with PostgreSQL.
GridSQL for EnterpriseDB Advanced Server (formerly ExtenDB)
sequoia (jdbc, formerly known as c-jdbc)
PL/Proxy - database partitioning system implemented as PL language.
HadoopDB - A MapReduce layer put in front of a cluster of postgres back end servers. Shared-nothing clustering.