So what's the idea behind a cluster?
You have multiple machines with the same copy of the DB where you spread the read/write? Is this correct?
How does this idea work? When I make a select query the cluster analyzes which server has less read/writes and points my query to that server?
When you should start using a cluster, I know this is a tricky question, but mabe someone can give me an example like, 1 million visits and a 100 million rows DB.
1) Correct. Every data node does not hold a full copy of the cluster data, but every single bit of data is stored on at least two nodes.
2) Essentially correct. MySQL Cluster supports distributed transactions.
3) When vertical scaling is not possible anymore, and replication becomes impractical :)
As promised, some recommended readings:
Setting Up Multi-Master Circular Replication with MySQL (simple tutorial)
Circular Replication in MySQL (higher-level warnings about conflicts)
MySQL Cluster Multi-Computer How-To (step-by-step tutorial, it assumes multiple physical machines, but you can run your test with all processes running on the same machine by following these instructions)
The MySQL Performance Blog is a reference in this field
1->your 1st point is correct in a way.But i think if multiple machines would share the same data it would be replication instead of clustering.
In clustering the data is divided among the various machines and there is horizontal partitioning means the dividing of the data is based on the rows,the records are divided by using some algorithm among those machines.
the dividing of data is done in such a way that each record will get a unique key just as in case of a key-value pair and each machine also has a unique machine_id related which is used to define which key value pair would go to which machine.
we call each machine a cluster and each cluster consists of an individual mysql-server, individual data and a cluster manager.and also there is a data sharing between all the cluster nodes so that all the data is available to the every node at any time.
the retrieval of data is done through memcached devices/servers for fast retrieval and
there is also a replication server for a particular cluster to save the data.
2->yes, there is a possibility because there is a sharing of all the data among all the cluster nodes. and also you can use a load balancer to balance the load.But the idea of load balancer is quiet common because they are being used by most of the servers. but if you are trying you just for your knowledge then there is no need because you will not get to notice the type of load that creates the requirement of a load balancer the cluster manager itself can do the whole thing.
3->RandomSeed is right. you do feel the need of a cluster when your replication becomes impractical means if you are using the master server for writes and slave for reads then at some time when the traffic becomes huge such that the sever would not be able to work smoothly then you will feel the need of clustering. simply to speed up the whole process.
this is not the only case, this is just one of the scenario this is only just a case.
hope this is helpful for you!!
Related
Just some contexts: In our old data pipeline system, we are running MySQL 5.6. or Aurora on Amazon rds. Bad thing about our old data pipeline is running a lot of heavy computations on the database servers because we are handcuffed by what was designed: treating transactional databases as data warehouse and our backend API directly “fishing” the databases heavily in our old system. We are currently patching this old data pipeline, while re-design the new data warehouse in SnowFlake.
In our old data pipeline system, the data pipeline calculation is a series of sequential MySQL queries. As our data grows bigger and bigger in the old data pipeline, what the problem now is the calculation might just hang forever at, for example, the step 3 MySQL query, while all metrics in Amazon CloudWatch/ grafana we are monitoring (CPU, database connections, freeable memory, network throughput, swap usages, read latency, available storage, write latency, etc. ) looks normal. The MySQL slow query log is not really helpful here because each of our query in the data pipeline is essentially slow anyway (can takes hours to run a query because the old data pipeline is running a lot of heavy computations on the database servers). The way we usually solve these problems is to “blindly” upgrade the MySQL/Aurora Amazon rds service and hoping it will solve the issue. I am wondering
(1) What are the recommended database metrics in MySQL 5.6. or Aurora on Amazon rds we should monitor real-time to help us identify why a query freezes forever? Like innodb_buffer_pool_size?
(2) Is there any existing tool and/or in-house approach where we can predict how many hardware resources we need before we can confidently execute a query and know it will succeed? Could someone share some 2 cents?
One thought: Since Amazon rds sometimes is a bit blackbox, one possible way is to host our own MySQL server on an Amazon EC2 instance in parallel to our Amazon MySQL 5.6/Aurora rds production server, so we can ssh into MySQL server and run a lot of command tools like mytop (https://www.tecmint.com/mysql-performance-monitoring/) to gather a lot more real time MySQL metrics which can help us triage the issue. Open to any 2 cents from gurus. Thank you!
None of the tools mentioned at that link should need to run on the database server itself, and to the extent that this is true, there should be no difference in their behavior if they aren't. Run them on any Linux server, giving the appropriate --host and --user and --password arguments (in whatever form they may expect). Even mysqladmin works remotely. Most of the MySQL command line tools do (such as the mysql cli, mysqldump, mysqlbinlog, and even mysqlcheck).
There is no magic coupling that most administrative utilities can gain by running on the same server as MySQL Server itself -- this is a common misconception but, in fact, even when running on the same machine, they still have to make a connection to the server, just like any other client. They may connect to the unix socket locally rather than using TCP, but it's still an ordinary client connection, and provides no extra capabilities.
It is also possible to run an external replica of an RDS/MySQL or Aurora/MySQL server on your own EC2 instance (or in your own data center, even). But this isn't likely to tell you a whole lot that you can't learn from the RDS metrics, particularly in light of the above. (Note also, that even replica servers acquire their replication streams using an ordinary client connection back to the master server.)
Avoid the temptation to tweak server parameters. On RDS, most of the defaults are quite sane, and unless you know specifically and precisely why you want to adjust a parameter... don't do it.
The most likely explanation for slow queries... is poorly written queries and/or poorly designed indexes.
If you are not familiar with EXPLAIN SELECT, then you need to learn it, live it, an love it. SQL is declarative, not procedural. That is, SQL tells the server what you want -- not specifically how to obtain it internall. For example: SELECT ... FROM x JOIN y tells the server to match up the rows from table x and y ON a certain criteria, but does not tell the server whether to read from x then find the matching rows in y... or read from y and find the matching rows in x. The net result is the same either way -- it doesn't matter which table the server examines first, internally -- but if the query or the indexes don't allow the server to correctly deduce the optimum path to the results you've requested, it can spend countless hours churning through unnecessary effort.
Take for an extreme and overly-simplified example, a table with millions of rows and table with 1 row. It would make sense to read the small table first, so you know what 1 value you're trying to join in the large table. It would make no sense to read throuh each row in the large table, then go over and check the small table for a match for each of the millions of rows. The order in which you join tables can be different than the order in which the actual joining is done.
And that's where EXPLAIN comes in. This allows you to inspect the query plan -- the strategy the internal query optimizer has concluded will get it to the answer you need with the least amount of effort. This is the core of the magic of relational database systems -- finding the correct solution in the optimal time, based on what it knows about the data. EXPLAIN shows you the order in which the tables are being accessed, how they're being joined, which indexes are being used, and an estimate of the number of rows from each table are involved -- and these numbers multiply together to give you an estimate of the number of permutations involved in resolving your query. Two small tables, each with 50,000 rows, joined without a proper index, means an entirely unreasonable 2,500,000,000 unique combinations between the two tables that must be evaluated; every row must be compared to every other row. In short, if this turns out to be the kind of thing that you are (unknowingly) asking the server to do, then you are definitely doing something wrong. Inspecting your query plan should be second nature any time you write a complex query, to ensure that the server is using a sensible strategy to resolve it.
The output is cryptic, but secret decoder rings are available.
https://dev.mysql.com/doc/refman/5.7/en/explain.html#explain-execution-plan
Basically, I'm working on a REST API for my application. I was researching for server performance, and I couldn't find information on splitting a database. Would it be better to have a single file system shared through multiple servers that use MySQL (if it's possible to store it on a shared file system), or should I just upgrade my current server or store different information on different databases (for example users a-n information on database 1 and o-z on another) if it's slowing it down? If anyone has more information on increasing database performance, or something I can read through it would be very much appreciated. Thanks in advance.
"Sharding" is where you put some of the rows of the main table on one physical server, some on other server(s). Only one system in a thousand needs this type of scaling.
"Partitioning" is where you split a single table into multiple "subtables" that act like a single table. There are very few use cases (I count only 4) where this provides any benefit. All the subtables live in the same server.
Having multiple MySQL instances on the same physical server adds complexity and may add some performance, but it is unlikely.
Having multiple MySQL instances on different physical server, but sharing the same data -- Do not attempt; MySQL does not know how to share its own data this way.
Using Replication lets you do arbitrary read scaling. This is slightly complex. It is a common approach. But it only handles apps that are more 'read' than 'write'.
Galera Clustering gives you some write scaling.
A single instance can handle lots of incoming requests (from a web server or other type of client). These clients could be scattered across multiple servers. That is scaling of clients, even without scaling MySQL, may be useful to you.
With a load balancer / proxy server / etc, you can also have multiple clients talking to multiple MySQL servers (read Slaves / Galera nodes / sharded servers / etc).
Many of the above techniques can be combined in the same system.
Bottom line: It smells like you don't yet know if you will need any scaling. When you get to that point, please provide more info on what the app is like so we can discuss the various options with less 'hand waving'.
I'm 99.999% certain all those words are just saying "premature optimization". Unless you have over 20GB of data (or 50GB or 100GB.. basically, a lot), use a single database and once it starts slowing down look at different options (sharding, etc).
And don't worry, you'll have plenty of other things to keep you busy without introducing advanced db tactics :)
I have a need to maintain a copy of an external database (including some additional derived data). With the same set of hardware, which one of the following solutions would give me faster consistency (low lag) with high availability? Assume updates to external database happen at 1000 records per second.
a) Create local mysql replica of an external db using mysql 5.7 replication (binary log file mechanism).
OR
b) Get real time Kafka events from external system, doing HTTP GET to fetch updated object details and use these details to maintain a local mysql replica.
The first will almost certainly give you lower lag (since there are just two systems and not three). Availability is about same - Kafka is high availability, but you have two databases on both sides anyway.
The second is better if you think you'll want to send the data in real-time to additional system. That is:
MySQL1 -> Kafka -> (MySQL2 + Elastic Search + Cassandra + ...)
I hate to answer questions with a 'just use this oddball thing instead' but I do worry you're gearing up a bit too heavy than you may need to -- or maybe you do, and I mis-read.
Consider a gossipy tool like serf.io . It's almost finished, and could give you exactly what you may need with something lighter than a kafka cluster or mysql pair.
I can't understand why separate write and read is better than write and read in one server.
For example, I have a mysql cluster with three machines: node1, node2,node3.
One possible architecture is:
All write requests to node1, but all read requests to node2 and node3.
The second possible architecture is:
All of these three nodes handle both writes and reads.
We can see in architecture one that the write to node1 pressure very huge, so I prefer architecture two.
Also, why does mongodb separates writes to primary node and reads to secondary nodes.
This is an issue of scale for both MySQL and MongoDB. In the simplest application with a small dataset and low traffic volume, having all writes and reads go to one server gives you a simple architecture. In a very high volume read application with low volume writes, a single write node replicating to more than one read nodes gives you the ability to scale your reads just by adding another node. In a high read AND write volume application you might consider sharding (in MySQL you do it yourself or find a tool to help), in mongodb you run mongos that handles sharding for you. Sharding will put records on a specific instance based on some key that you define. The key will determine the instance each record should be stored on. You can imagine that sharding would be more complicated to manage than a single server for read/write access. You would be right, even in a case like mongodb that does the sharding for you once you define a key (or just use the default key).
MySQL Cluster also supports auto-sharding - by default hashing the primary key, but users can feed their own keys in to provide more distribution awareness. Each node in the cluster is a master, and internal load balancing will distribute the loads across nodes
While very high level, the short demo posted here introduces you to the concepts of sharding in MySQL Cluster:
http://www.oracle.com/pls/ebn/swf_viewer.load?p_shows_id=11464419
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.