Evaluating a BI option for a client, wanted to check common approaches.
If the answer is 'multiplex' then a couple of additional questions
a) why does SAP insist this is neither a share-nothing, nor share-everything architecture, while the rule of - a node going down doesn't affect the cluster - implies you are running in the latter (share-everything). Unless every node is required to have a single failover
b) how complicated in your experience was the administration of a multiplex
I have run Sybase, where each instance would have a single dedicated node in multiple replication and standby configurations, but never in this "hybrid" cluster.
Related
What is database clustering? If you allow the same database to be on 2 different servers how do they keep the data between synchronized. And how does this differ from load balancing from a database server perspective?
Database clustering is a bit of an ambiguous term, some vendors consider a cluster having two or more servers share the same storage, some others call a cluster a set of replicated servers.
Replication defines the method by which a set of servers remain synchronized without having to share the storage being able to be geographically disperse, there are two main ways of going about it:
master-master (or multi-master) replication: Any server can update the database. It is usually taken care of by a different module within the database (or a whole different software running on top of them in some cases).
Downside is that it is very hard to do well, and some systems lose ACID properties when in this mode of replication.
Upside is that it is flexible and you can support the failure of any server while still having the database updated.
master-slave replication: There is only a single copy of authoritative data, which is the pushed to the slave servers.
Downside is that it is less fault tolerant, if the master dies, there are no further changes in the slaves.
Upside is that it is easier to do than multi-master and it usually preserve ACID properties.
Load balancing is a different concept, it consists distributing the queries sent to those servers so the load is as evenly distributed as possible. It is usually done at the application layer (or with a connection pool). The only direct relation between replication and load balancing is that you need some replication to be able to load balance, else you'd have a single server.
From SQL Server point of view:
Clustering will give you an active - passive configuration. Meaning in a 2 node cluster, one of them will be the active (serving) and the other one will be passive (waiting to take over when the active node fails). It's a high availability from hardware point of view.
You can have an active-active cluster, but it will require multiple instances of SQL Server running on each node. (i.e. Instance 1 on Node A failing over to Instance 2 on Node B, and instance 1 on Node B failing over to instance 2 on Node A).
Load balancing (at least from SQL Server point of view) does not exists (at least in the same sense of web server load balancing). You can't balance load that way. However, you can split your application to run on some database on server 1 and also run on some database on server 2, etc. This is the primary mean of "load balancing" in SQL world.
Clustering uses shared storage of some kind (a drive cage or a SAN, for example), and puts two database front-ends on it. The front end servers share an IP address and cluster network name that clients use to connect, and they decide between themselves who is currently in charge of serving client requests.
If you're asking about a particular database server, add that to your question and we can add details on their implementation, but at its core, that's what clustering is.
Database Clustering is actually a mode of synchronous replication between two or possibly more nodes with an added functionality of fault tolerance added to your system, and that too in a shared nothing architecture. By shared nothing it means that the individual nodes actually don't share any physical resources like disk or memory.
As far as keeping the data synchronized is concerned, there is a management server to which all the data nodes are connected along with the SQL node to achieve this(talking specifically about MySQL).
Now about the differences: load balancing is just one result that could be achieved through clustering, the others include high availability, scalability and fault tolerance.
I know there have been many articles written about database replication. Trust me, I spent some time reading those articles including this SO one that explaints the pros and cons of replication. This SO article goes in depth about replication and clustering individually, but doesn't answer these simple questions that I have:
When do you replicate your database, and when do you cluster?
Can both be performed at the same time? If yes, what are the inspirations for each?
Thanks in advance.
MySQL currently supports two different solutions for creating a high availability environment and achieving multi-server scalability.
MySQL Replication
The first form is replication, which MySQL has supported since MySQL version 3.23. Replication in MySQL is currently implemented as an asyncronous master-slave setup that uses a logical log-shipping backend.
A master-slave setup means that one server is designated to act as the master. It is then required to receive all of the write queries. The master then executes and logs the queries, which is then shipped to the slave to execute and hence to keep the same data across all of the replication members.
Replication is asyncronous, which means that the slave server is not guaranteed to have the data when the master performs the change. Normally, replication will be as real-time as possible. However, there is no guarantee about the time required for the change to propagate to the slave.
Replication can be used for many reasons. Some of the more common reasons include scalibility, server failover, and for backup solutions.
Scalibility can be achieved due to the fact that you can now do can do SELECT queries across any of the slaves. Write statements however are not improved generally due to the fact that writes have to occur on each of the replication member.
Failover can be implemented fairly easily using an external monitoring utility that uses a heartbeat or similar mechanism to detect the failure of a master server. MySQL does not currently do automatic failover as the logic is generally very application dependent. Keep in mind that due to the fact that replication is asynchronous that it is possible that not all of the changes done on the master will have propagated to the slave.
MySQL replication works very well even across slower connections, and with connections that aren't continuous. It also is able to be used across different hardware and software platforms. It is possible to use replication with most storage engines including MyISAM and InnoDB.
MySQL Cluster
MySQL Cluster is a shared nothing, distributed, partitioning system that uses synchronous replication in order to maintain high availability and performance.
MySQL Cluster is implemented through a separate storage engine called NDB Cluster. This storage engine will automatically partition data across a number of data nodes. The automatic partitioning of data allows for parallelization of queries that are executed. Both reads and writes can be scaled in this fashion since the writes can be distributed across many nodes.
Internally, MySQL Cluster also uses synchronous replication in order to remove any single point of failure from the system. Since two or more nodes are always guaranteed to have the data fragment, at least one node can fail without any impact on running transactions. Failure detection is automatically handled with the dead node being removed transparent to the application. Upon node restart, it will automatically be re-integrated into the cluster and begin handling requests as soon as possible.
There are a number of limitations that currently exist and have to be kept in mind while deciding if MySQL Cluster is the correct solution for your situation.
Currently all of the data and indexes stored in MySQL Cluster are stored in main memory across the cluster. This does restrict the size of the database based on the systems used in the cluster.
MySQL Cluster is designed to be used on an internal network as latency is very important for response time.
As a result, it is not possible to run a single cluster across a wide geographic distance. In addition, while MySQL Cluster will work over commodity network setups, in order to attain the highest performance possible special clustering interconnects can be used.
In Master-Salve configuration the write operations are performed by Master and Read by slave. So all SQL request first reaches the Master and a queue of request is maintained and the read operation get executed only after completion of write. There is a common problem in Master-Salve configuration which i also witnessed is that when queue becomes too large to be maintatined by master then this achitecture collapse and the slave starts behaving like master.
For clusters i have worked on Cassandra where the request reaches a node(table) and a commit hash is maintained which notices the differences made to a node and updates the other nodes based on that commit hash. So here all operations are not dependent on a single node.
We used Master-Salve when write data is not big in size and count otherwise we use clusters.
Clusters are expensive in space and Master-Salve in time so your desicion of what to choose depends on what you want to save.
We can also use both at the same time, i have done this in my current company.
We moved the tables with most write operations to Cassandra and we have written 4 API to perform the CRUD operation on tables in Cassandra. As whenever an HTTP request comes it first hits our web server and from the code running on our web server we can decide which operation has to be performed (among CRUD) and then we call that particular API to make changes to the cassandra database.
My exposure to NoSQL or NewSQL/NeoSQL database servers is extremely limited, only theoretical. I've worked with traditional RDBMSs (like MySQL, Postgres) and directory-server (OpenLDAP), with and without replication.
My application stack is based on JBoss, and I've been tasked with setting up a minimal demo (with our application) that can demonstrate durability and high-availability of data, in VoltDB. Performance testing, is not an objective at all.
Have been going thru the VoltDB Planning Guide, but I am confused between the "+1" or "x2" in terms of number of servers (or VoltDB instances) required. Especially given these 2 statements:-
The easiest way to size hardware for a K-Safe cluster is to size the
initial instance of the database, based on projected throughput and
capacity, then multiply the number of servers by the number of
replicas you desire (that is, the K-Safety value plus one).
Rule of Thumb
When using K-Safety, configure the number of cluster nodes as a whole multiple of the number of copies of the database
(that is, K+1)
Questions:
Now, let's say that I need 1 server given capacity/throughput
requirements. So, to be able to have durability and
high-availability, do I need: 2, 3 or 4 servers ?
OTOH, using just 1 server, what all key features of VoltDB would I
have to forgo ?
Is there any relationship (or conflict) between VoltDB's full
disk-persistence and snapshots ? Say, the availability of disk-persistence
removes the need for snapshots ?
If you use 2 servers, you can keep a synchronous replica of data to protect from data loss, much like a RAID1 hard drive. Your data is double-safe, but there is a catch with availability. With only two servers, it's impossible to differentiate a network split from a failed node. In some cases, VoltDB will shut down a live node when another fails to ensure there will be no split brain. With 3 nodes, this won't be an issue and the cluster will remain available after any single node failure (with k=1 or k=2).
With just 1 server, all you lose is the multiple copies of data on multiple servers and the high-availability features that allow VoltDB to continue running after a node failure. You still have all of the other VoltDB features, including full disk persistence.
I have a question for the DBA's out there: If I scale from a single web/DB server setup to two web/two DB server setup with a load balancer in front of the web servers to route incoming queries evenly... how do solutions like MySQL Cluster work so that a change made to one DB server is immediately known to the other (otherwise, users routed to the other DB server won't see the data or will outdated data), or at least so that the other web server is made aware of the fact that it's reading "dirty data" and it should try again in X seconds so as to get up-to-date data?
Thank you.
TWO ways of doing this.
Active/Active or Active/Passive.
Active/Passive is most prevalent
The data is kept in sync on the passive node.
The cluster is useful configuration in as much as the active node goes down the passive is immediately switched hence no downtime.
The clustering continuously synchronises the 2 nodes in the cluster.
I work with SQL server but I think the basic premise of clustering is the same for mySQL - that is no (or no noticeable) downtime on hardware failure.
EDIT: Additionally the clustering software handles the synchronisation. You don't need to worry. You view the cluster nodes as a virtual directory, which behaves like one server in windows.
here is document explaining this
http://www.sql-server-performance.com/articles/clustering/clustering_intro_p1.aspx
In Windows server clustering (to be distinguished from High Performance Clustering), there is a shared external storage array. The active node takes ownership/control of the storage, and when that node fails, the storage 'fails over' to the previously passive node (which is now the active node). There are also different schemes that allow for independent storage at each node, vs. shared storage. However, these require the application to have enough intelligence to know that it is clustered, and keep the two storage sets in sync.
Clustering is also where a number of nodes handle the workload, this is sometimes called active/active clusters i.e. all the nodes share the workload and are active. This is normally handled by specialist software like Oracle RAC (RAC#Wikipedia) for the Oracle RDBMS database. RAC allows Oracle to scale to very large workloads.
What is database clustering? If you allow the same database to be on 2 different servers how do they keep the data between synchronized. And how does this differ from load balancing from a database server perspective?
Database clustering is a bit of an ambiguous term, some vendors consider a cluster having two or more servers share the same storage, some others call a cluster a set of replicated servers.
Replication defines the method by which a set of servers remain synchronized without having to share the storage being able to be geographically disperse, there are two main ways of going about it:
master-master (or multi-master) replication: Any server can update the database. It is usually taken care of by a different module within the database (or a whole different software running on top of them in some cases).
Downside is that it is very hard to do well, and some systems lose ACID properties when in this mode of replication.
Upside is that it is flexible and you can support the failure of any server while still having the database updated.
master-slave replication: There is only a single copy of authoritative data, which is the pushed to the slave servers.
Downside is that it is less fault tolerant, if the master dies, there are no further changes in the slaves.
Upside is that it is easier to do than multi-master and it usually preserve ACID properties.
Load balancing is a different concept, it consists distributing the queries sent to those servers so the load is as evenly distributed as possible. It is usually done at the application layer (or with a connection pool). The only direct relation between replication and load balancing is that you need some replication to be able to load balance, else you'd have a single server.
From SQL Server point of view:
Clustering will give you an active - passive configuration. Meaning in a 2 node cluster, one of them will be the active (serving) and the other one will be passive (waiting to take over when the active node fails). It's a high availability from hardware point of view.
You can have an active-active cluster, but it will require multiple instances of SQL Server running on each node. (i.e. Instance 1 on Node A failing over to Instance 2 on Node B, and instance 1 on Node B failing over to instance 2 on Node A).
Load balancing (at least from SQL Server point of view) does not exists (at least in the same sense of web server load balancing). You can't balance load that way. However, you can split your application to run on some database on server 1 and also run on some database on server 2, etc. This is the primary mean of "load balancing" in SQL world.
Clustering uses shared storage of some kind (a drive cage or a SAN, for example), and puts two database front-ends on it. The front end servers share an IP address and cluster network name that clients use to connect, and they decide between themselves who is currently in charge of serving client requests.
If you're asking about a particular database server, add that to your question and we can add details on their implementation, but at its core, that's what clustering is.
Database Clustering is actually a mode of synchronous replication between two or possibly more nodes with an added functionality of fault tolerance added to your system, and that too in a shared nothing architecture. By shared nothing it means that the individual nodes actually don't share any physical resources like disk or memory.
As far as keeping the data synchronized is concerned, there is a management server to which all the data nodes are connected along with the SQL node to achieve this(talking specifically about MySQL).
Now about the differences: load balancing is just one result that could be achieved through clustering, the others include high availability, scalability and fault tolerance.