I am a Java developer. I want to know what is the best way to store huge data into mysql using Java.
Huge: two hundred thousand talk messages every second.
An index is not needed here
Should I store the messages into the database as soon as the user creates them? Will it be too slow?
1 billion writes / day is about 12k / second. Assuming each message is about 16 bytes, that's about 200k / sec. If you don't care about reading, you can easily write this to disk at this rate, maybe one message per line. Your read access pattern is probably going to dictate what you end up needing to do here.
If you use MySQL, I'd suggest combining multiple messages per row, if possible. Partitioning the table would be helpful to keep the working set in memory, and you'll want to commit a number of records per transaction, maybe 1000 rows. You'll need to do some testing and tuning, and this page will be helpful:
http://dev.mysql.com/doc/refman/5.0/en/insert-speed.html
You should probably also look at Cassandra which is written with heavy write workloads in mind.
My suggestion is also MongoDB. Since NoSQL paradigm fits your needs perfectly.
Below is a flavor of MongoDB in Java -
BasicDBObject document = new BasicDBObject();
document.put("database", "mkyongDB");
document.put("table", "hosting");
BasicDBObject documentDetail = new BasicDBObject();
documentDetail.put("records", "99");
documentDetail.put("index", "vps_index1");
documentDetail.put("active", "true");
document.put("detail", documentDetail);
collection.insert(document);
This tutorial is for good to get started. You can download MongoDB from github.
For optimization of MongoDB please refer this post.
Do you have to absolutely use MySQL or Are you open to other DBs as well? MongoDb or CouchDB will be a good fit for these kind of needs. Check them out if you are open to other DB options.
If you have to go absolutely with MySql, then we have done something similar all the related text messages go in a child as single json. We append to it every time and we keep master in a separate table. So one master and one child record at the minimum and more child records as the messages go beyond certain number ( 30 in our scenario) , implemented kind of "load more.." queries second child record which holds 30 more.
Hope this helps.
FYI, we are migrating to CouchDB for some other reasons and needs.
There are at least 2 different parts to this problem:
Processing the messages for storage in the database
What type of storage to use for the message
For processing the messages, you're likely going to need a horizontally scalable system (meaning you can add more machines to process the messages quickly) so you don't accumulate a huge backlog of messages. You should definitely not try to write these messages synchronously, but rather when a message is received, put it on a queue to be processed for writing to the database (something like JMS comes to mind here).
In terms of data storage, MySQL is a relational database, but it doesn't sound like you are really doing any relational data processing, rather just storing a large amount of data. I would suggest looking into a NoSQL database (as others have suggested here as well) such as MongoDB, Cassandra, CouchDB, etc. They each have their strengths and weaknesses (you can read more about each of them on their respective websites and elsewhere on the internet).
I guess, typical access would involve retrieving all text of one chat session at least.
The number of rows is large and your data is not so much relational. This is a good fit for Non-Relational database.
If you still want to go with MySQL, use Partitions. While writing, use batch inserts and while reading provide sufficient Partition pruning hints in your queries. Use EXPLAIN PARTITIONS to check whether partitions are being pruned. In this case I would strongly recommend that you combine chat lines of a one chat session into a single row. This will dramatically reduce the number of rows as compared to one chat line per row.
You didn't mention how many many days of data you want to store.
On a separate note: How successful would your app have to be in terms of users to require 200k messages per second? An active chat session may generate about 1 message every 5 seconds from one user. For ease of calculation lets make it 1 second. So you are building capacity for 200K online users. Which implies you would at least have a few million users.
It is good to think of scale early. However, it requires engineering effort. And since resources are limited, allocate them carefully for each task (Performance/UX etc). Spending more time on UX, for example, may yield a better ROI. When you get to multi-million user territory, new doors will open. You might be funded by an Angel or VC. Think of it as a good problem to have.
My 2 cents.
Related
Give this
public void do(RequestObject request, Callback<RequestObject> callback);
Where Callback is called when the request is processed. One client has to set status of the request to the database. The client fetches some items passes them to the above method and the callback sets the status.
It was working ok for small number of items and slower IO. But now, the IO is speed up and the status is written to database vary frequently. This is causing my database (MySQL) to make so many disk read write calls. My disk usage goes through the roof.
I was thinking of aggregating the setting of status but power in not reliable, that is not a plausible solution. How should re'design this?
EDIT
When the process is started I insert a value and when there is an update, I fetch the item and update the item. #user2612030 Your question lead me to believe, using hibernate might be what is causing more reads than it is necessary.
I can upgrade my disk drive to SSD but that would only do so much. I want a solution that scales.
An SSD is a good starting point, more RAM to MySQL should also help. It can't get rid of the writes, but with enough RAM (and MySQL configured to use it!) there should be few physical reads. If you are using the default configuration, tune it. See for example https://www.percona.com/blog/2016/10/12/mysql-5-7-performance-tuning-immediately-after-installation/ or just search for MySQL memory configuration.
You could also add disks and spread the writes to multiple disks with multiple controllers. That should also help a bit.
It is hard to give good advice without knowing how you record status values. Inserts or updates? How many records are there? Data model? However, to really scale you need to shard the data somehow. That way one server can handle data in one range and another server data in another range and so on.
For write-heavy applications that is non-trivial to set up with MySQL unless you do the sharding in the application code. Most solutions with replication work best for read-mostly applications. You may want to look into a NoSQL database, for example MongoDB, that has been designed for distributing writes from the outset. MongoDB has other challenges (eventual consistency), but it can deliver scalable writes.
In Java code I am trying to fetch 3500 rows from DB(Oracle). It takes almost 15 seconds to load the data. I have approached storing the result in Cache and retrieving from it too. I am using simple Select statement and displaying 8 columns from a single table (No joins used) .Using List to save the data from DB and using it as source for Datatable. I have also thought from hardware side such as RAM capacity, Storage, Network speed etc... It exceeds the minimum requirements comfortably. Can you help to do it quicker (Shouldn't take more than 3 seconds)?
Have you implemented proper indexing to your tables? I don't like to ask this since this is a very basic way of optimizing your tables for queries and you mention that you have already tried several ways. One of the workarounds that works for me is that if the purpose of the query is to display the results, the code can be designed in such a way that the query should immediately display the initial data while it is still loading more data. This implies to implement a separate thread for loading and separate thread for displaying.
It is most likely that the core problem is that you have one or more of the following:
a poorly designed schema,
a poorly designed query,
an badly overloaded database, and / or
a badly overloaded / underprovisioned network connection between the database and your client.
No amount of changing the client side (Java) code is likely to make a significant difference (i.e. a 5-fold increase) ... unless you are doing something crazy in the way you are building the list, or the bottleneck is in the display code not the retrieval.
You need to use some client-side and server-side performance tools to figure out whether the real bottleneck is the client, the server or the network. Then use those results to decide where to focus your attention.
I have 30 GB of twitter data stored in CouchDB. I am aiming to process each tweet in java but the java program is not able to hold such a large data at a time. In order to process the entire dataset, I am planning to divide my entire dataset into smaller ones with the help of filtered replication supported by CouchDb. But, as I am new to couchDB, I am facing a lot of problems in doing so. Any better ideas for doing it are welcome. Thanks.
You can always query couchdb for a dataset that is small enough for your java program, so there should be no reason to replicate subsets to smaller databases. See this stackoverflow answer for a way to get paged results from couchdb. You might even employ couchdb itself for the processing with map/reduce, but that depends on your problem.
Depending on the complexity of the queries and the changes you make when processing your data set you should be fine with one instance.
As the previous poster you can use paged results, I tend to do something different:
I have a document for social likes. The latter always refers to a URL and I want to try and have an update at every 2-3 hours.
I have a view that sorts URL's by the documents by the age of the last update request and the last update.
I query this view so that I exclude the articles that had a request within 30 minutes or have been updated less than 2 hours ago.
I use rabbit MQ when enqueuing the jobs and if these are not picked up within 30 minutes, they expire.
Just FYI, this question is not exactly based on MongoDB, but happens to use MongoDB. I am assuming we might end up using MongoDB's feature such as sharding in a good design, hence mentioning about MongoDB. Also fwiw, we use Java.
So we have around 100 million records in a certain collection, of which we need to select all the items which have some data set to tomorrow. Usually this query returns 10 million records.
You can think that we have N (say ten) machines at our hand. We can assume, MongoDB is sharded based on record_id.
The each record that we will process is independent of the other records we are reading. No records will be written as part of this batch job.
What I am looking to do is,
Not to centralize workload distribution across different machine.
Fair or almost fair workload distribution
(not sure if the following requirement could be fullfilled without compromising requirement.1)
Fault tolerance (if one of the batch machine is down we want other machine to take its load.)
Any good solution, which has already worked in similar situation ?
I can speak in context of MongoDB
Requirements 1 and 2 is done through sharding. I'm not sure if I follow your question though as it sounds like 1 says you don't want to centralize workload and 2 is that you want to distribute work load evenly.
In any case, with the proper shard key, you will distribute your workload across your shards. http://docs.mongodb.org/manual/sharding/
Requirement 3 is performed via replica sets in MongoDB. http://docs.mongodb.org/manual/replication/
I would have to understand your application and use case more to know for certain, but pulling 10M records for a 100M record as your typical access pattern doesn't sound like the right document model is in place. Keep in mind that collection <> table and document <> record. I would look into storing your 10M records at a higher logical granularity so you pull less records; this will significantly improve performance.
I have a Java application that needs to display large amounts of data (on the order of 1 million data points). The data doesn't all need to be displayed at the same time but rather only when requested by a user. The app is a desktop app that is not running with an app server or hitting any centralized database.
My thought was to run a database on the machine and load the data in there. The DB will be read only most of the time, so I should be able to index to help optimize queries. If I'm running on a local system, I'm not sure if I should try and implement some caching (I'm not sure how fast the queries will run, I'm currently working on them).
Is this is a logical way to approach the problem or would there be better approaches?
Thanks,
Jeff
Display and data are two different things.
You don't give any details about either, but it could be possible to generate the display in the background, bringing in the data one slice at a time, and then displaying when it's ready. Lots of anything could cause memory issues, so you'll need to be careful. The database will help persist things, but it won't help you get ten pounds of data into your five pound memory bag.
UPDATE: If individuals are only reading a few points at a time, and display isn't an issue, then I'd say that any database will be able to handle it if you index the table appropriately. One million rows isn't a lot for a capable database.
Embedded DB seems reasonable. Check out JavaDB/Derby or H2 or HSQLDB.
Sqlite with a java wrapper is fine too.
It really depends on your data. Do multiple instances request the data? If not, it is definitely worth to look for a simple SQLite database as the storage. It is just a single file on your file system. No need to set up a server.
Well, depends on data size. 1 Million integers for example isnt that much, but 1 Million data structures/classes or whatever with, lets say, 1000 Bytes size is much.
For small data: keep them in memory
For large data: i think using the DB would be good.
Just my opinion :)
edit:
Of course it depends also on the speed you want to achieve. If you really need high speed and the data is big you could also cache some of them in memory and leave the rest in the db.