I'm reading everywhere that the minimum for a mysql 7 cluster is 3 physical machines, but the cluster exists out of 4 nodes
1 mysql node
2 data nodes
1 management node
So this means at least 1 machine must be hosting 2 types of nodes but I cannot find anywhere which machine shares which nodes.
I've read that sharing MySQL and data nodes is not recommended so then it must be the management node and MySQL node which are sharing a machine?
Could anyone please advice me on this..
Just a small edit: I'm currently setting this up because we now have 1 normal MySQL server and we're pretty much hitting its limit. I'm mainly trying to setup the cluster for performance gain (2 data/MySQL nodes should be faster then 1 right?), expanding it with more server to gain redundancy is next on the list.
You can co-locate the management nodes with the SQL nodes to reduce your footprint to 3 x physical hosts
I would recommend taking a look at the Evaluation Guide (note, opens a pdf) which can talk you through these factors, as well as providing some tips / best practices when moving from a single MySQL node to a fully distributed storage engine such as MySQL Cluster:
http://dev.mysql.com/downloads/MySQL_Cluster_72_DMR_EvaluationGuide.pdf
Related
I have a problem with my cluster couchbase, I have 4 (four) servers in my cluster, I have 3 (three) replicated number, that is the original data and + 3 copies, my created documents are 100% available for consultation while the cluster contains the 4 nodes, what caught my attention and generated very strange is the fact that if one of the machines is unavailable the data of the document are not available or are located when I perform a search.
To improve understanding, I have this cluster with 4 servers to ensure resilience and high availability of data, but it is to lose one of the machines and the data is no longer found when I execute query in one of the 3 replicas.
My 4 nodes of this server are with data, index and query services enabled.
Has anyone gone through this before?
this my cluster services
in this image, my big question is if this option would help "View index replicas"
I've started to use Google Cloud SQL and I need to improve my IOPS and network speed. I've seen that this it's only possible improving the type of machine and/or improving the size of disk. And this is my question. In my case, I need to migrate 2 MySQL databases (from 2 different projects) and I don't know what is better: 1 big instance with 2 databases? 2 small instances with the database in each instance? or 1 regular instance + 1 read replica instance?
Thank you in advance!
The answer is usual "It depends".
If you're not concerned with data isolation issues, a single instance would be more efficient and easier to manage.
If you split data between instances, you're also capping performance per database. This can be a non-issue if your datasets are similar and process the same amount of requests.
Read replicas could be a solution to scale IOPS if your application workload is heavily skewed towards reads.
Also, independent of which option you will choose, consider HA-setup.
I'm posed with a simple problem of scaling. As college students, we are new to setting up AWS services, droplets, scaling etc. We are stuck at deciding the architecture of our app. I'm unable to decide whether to use a big computing AMAZON EC2 or smaller multiple instances for benchmarking performance.
Even after code optimization, our MySQL queries are not as fast as we want it to be and clearly our hardware will address this problem. We are looking for high performance servers which require mostly searching a lot of MySQL FULL INDEXED search queries over 1.3M records (which clearly is a CPU and Memory intensive task). We intend to switch over to Solr at a later point of time. Both these tasks are very CPU demanding and RAM dependent. As of now, we are running our web app stack entirely on a single CPU with 2 cores and 4 GB RAM. However, we now wish to split the load up into multiple, say 5 instances/droplets of each 2 cores and 4 GB RAM.
Our primary concern is that, If we did create multiple ec2 instances/ droplets, wouldn't there be a considerable overhead for communicating between the instances/droplets for a standard MySQL search. As far as I know, the MySQL connection uses sockets to connect to local/remote host. Being a remote communication between 4 servers, I would expect significant overhead for EACH query.
For instance, let's say I've setup 4 instances and I've been allocated these IP's for each of them.
Server 1- x.x.x.1
Server 2- x.x.x.2
Server 3 - x.x.x.3
Server 4 - x.x.x.4
I setup a MySQL server and dump my databases into each of these instances (sounds like a very bad idea). Now I make a MySQL connection using python as:
conn1 = MySQLdb.connect(host=server1,user=user,passwd=password,db=db)
conn2 = MySQLdb.connect(host=server2,user=user,passwd=password,db=db)
conn3 = MySQLdb.connect(host=server3,user=user,passwd=password,db=db)`
conn4 = MySQLdb.connect(host=server4,user=user,passwd=password,db=db)
Since, each of these databases arn't on the localhost, I would guess that there is a huge overhead involved in contacting the server and getting the data for each query.
My Thoughts:
I'm guessing there must be a solution for integrating different droplets/ instances together. Unfortunately, I haven't found any resources to support that claim.
I've looked into Amazon RDS, which seems like a good fit. But again, I wouldn't be able to benchmark against a 4 instances MySQL search or a single huge AWS RDS server (given, it is quite expensive for new apps.)
We are also unsure of replacement of python with popular languages for scaling such as Scala which will help me tackle this problem of dealing with multiple servers.
Any suggestions will be greatly appreciated by our 3 member team :)
I intend to run a Point of Sale software in a galera cluster (percona xtradb). Each POS terminal would be its own cluster and then there will be an Amazon EC2 in addition to help avoid split-brain scenarios.
Is the above setup an ideal cluster setup? My POS terminals could range from 1 to N nodes within a local network and I will always only have 1 EC2 instance outside the network.
Thanks,
Yes. To provide automatic failover, 3 nodes is required. If you have 3 nodes in the same building, etc, then you are not safe against floods, earthquakes, tornadoes, data center failure, etc. "Within the local network" -- see what Amazon means by that, then read between the lines; it may or may not protect you from various possible disasters.
Do not plan on having "too many" nodes in the cluster -- all writes go to all other nodes; this can add up to a lot of network traffic. (I have not heard of more than something like a dozen nodes. But I don't know what the practical limit is.)
You could have multiple clusters and have data replicated off-cluster to some central server for reporting, etc. That replication would be ordinary MySQL replication, not the Galera type.
I'm building a very small NDB cluster with only 3 machines. This means that machine 1 will serve as both MGM Server, MySQL Server, and NDB data node. The database is only 7 GB so I plan to replicate each node at least once. Now, since a query might end up using data that is cached in the NDB node on machine one, even if it isn't node the primary source for that data, access would be much faster (for obvious reasons).
Does the NDB cluster work like that? Every example I see has at least 5 machines. The manual doesn't seem to mention how to handle node differences like this one.
There are a couple of questions here :
Availability / NoOfReplicas
MySQL Cluster can give high availability when data is replicated across 2 or more data node processes. This requires that the NoOfReplicas configuration parameter is set to 2 or greater. With NoOfReplicas=1, each row is stored in only one data node, and a data node failure would mean that some data is unavailable and therefore the database as a whole is unavailable.
Number of machines / hosts
For HA configurations with NoOfReplicas=2, there should be at least 3 separate hosts. 1 is needed for each of the data node processes, which has a copy of all of the data. A third is needed to act as an 'arbitrator' when communication between the 2 data node processes fails. This ensures that only one of the data nodes continues to accept write transactions, and avoids data divergence (split brain). With only two hosts, the cluster will only be resilient to the failure of one of the hosts, if the other host fails instead, the whole cluster will fail. The arbitration role is very lightweight, so this third machine can be used for almost any other task as well.
Data locality
In a 2 node configuration with NoOfReplicas=2, each data node process stores all of the data. However, this does not mean that only one data node process is used to read/write data. Both processes are involved with writes (as they must maintain copies), and generally, either process could be involved in a read.
Some work to improve read locality in a 2-node configuration is under consideration, but nothing is concrete.
This means that when MySQLD (or another NdbApi client) is colocated with one of the two data nodes, there will still be quite a lot of communication with the other data node.