My boss is having me create a database table that keeps track of some of our inventory with various parameters. It's meant to be implemented as a cron job that runs every half hour or so, but the scheduling part isn't important since we've already discussed that we're handling it later.
What I'm want to know is if it's more efficient to just delete everything in the table each time the script is called and repopulate it, or go through each record to determine if any changes were made and update each entry accordingly. It's easier to do the former, but given that we have over 700 separate records to keep track of, I don't know if the time it takes to do this would put a huge load on the server. The script is written in PHP.
700 records is an extremely small number of records to have performance concerns. Don't even think about it, do whichever is easier for you.
But if it is performance that you are after, updating rows is slower than inserting rows, (especially if you are not expecting any generated keys, so an insertion is a one-way operation to the database instead of a roundtrip to and from the database,) and TRUNCATE TABLE tends to be faster than DELETE * FROM.
If you have IDs for the proper inventory talking about SQL DB, then it would be good practice to update them, since in theory your IDs will get exhausted (overflow).
Another approach would be to use some NoSQL DB like MongoDB and simply update the DB with given json bodies apparently with existing IDs, and the DB itself will figure it out on its own.
Related
So i have database in project Mysql .
I have a main table that have main staff for updating and inserting .
I have huge data traffic on the data . what i am doing mainly reading .csv file and inserting to table .
Everything works file for 3 days but when table record goes above 20 million the database start responding slow , and in 60 million more slow.
What i have done ?
I have applied index in the record where i think i need of it . (where clause field for fast searching) .
I think query optimisation can not be issue because database working fine for 3 days and when data filled in table it get slow . and as i reach 60 million it work more slow .
Can you provide me the approach how can i handle this ?
What should i do ? Should i shift data in every 3 days or what ? What you have done in such situation .
The purpose of database is to store a huge information. I think the problem is not in your database, it should be poor query, joins, Database buffer, index and cache. These are the following reason which makes your response to slow up. For more info check this link
I have applied index in the record where i think i need of it
Yes, index improve the performance of SELECT query, but at the same time it will degrade your DML operation and index has to be restructure whenever you perform any changes to indexed column.
Now, this is totally depending on your business need, whether you need index or not, whether you can compromise SELECT or DML.
Currently, many industries uses two different schemas OLAP for reporting and analytics and OLTP to store real-time data (including some real-time reporting).
First of all it could be helpful for us to now which kind of data you want to store.
Normally it makes no sense to store such a huge amount of data in 3 days because no one ever will be able to use this in an effective way. So it is better to reduce the data before storing in the database.
e.g.
If you get measuring values from a device which gives you one value a millisecond, you should think if any user is ever asking for a special value at a special millisecond or if it not makes more sense to calculate the average value of once a second, minute or hour or perhaps once a day?
If you really need the milliseconds but only if the user takes a deeper look, you can create a table from the main table with only the average values of an hour or day or whatever and work with that table. Only if the user goes in ths "milliseconds" view you use the main table and have to live with the more bad performance.
This all is of course only possible if the database data is read only. If the data in the database is changed from the application (and not only appended by the CSV import) then using more then one table will be error prone.
Whick operation do you want to speed up?
insert operation
A good way to speed it up is to insert records in batch. For example, insert 1000 records in each insert statement:
insert into test values (value_list),(value_list)...(value_list);
other operations
If your table got tens of millions of records, everything will be slowing down. This is quite common.
To speed it up in this situation, here is some advice:
Optimize your table definition. It depends on your particular case. Creating indexes is a common way.
Optimize your SQL statements. Apparently a good SQL statement will run much faster, and a bad SQL statement might be a performance killer.
Data migration. If only part of your data is used frequently, you can shift the infrequently-used data to another big table.
Sharding. This is a more complicated way, but usually used in big data system.
For the .csv file, use LOAD DATA INFILE ...
Are you using InnoDB? How much RAM do you have? What is the value of innodb_buffer_pool_size? That may not be set right -- based on queries slowing down as the data increases.
Let's see a slow query. And SHOW CREATE TABLE. Often a 'composite' index is needed. Or reformulation of the SELECT.
I've got a ruby scripts which imports XML files to a MySQL database. It does it by looping through the elements in the XML file and finally
table.where(
value: e['value'],
...
).first_or_create
The script has to process a lot of data, most of it is already in the database. Because of this, it runs really slow because first_or_create obviously triggers a lot ot SELECT queries.
Is there any way to handle this more rapidly? Is it related to connection management?
Thanks
first_or_create is of course a convenience method, which doesn't much care about performance on a bigger data set.
Ensure all your indices are in place.
First obvious way to increase performance would be: since every create statement is wrapped in a begin, commit transaction block. Thats 3 queries for one insert.
You can place your whole loop inside transaction block - that will gain you some time as it will only execute begin and commit once.
Remember that the roundtrip to/from database takes considerable amount of time, so an obvious performance boost is to combine multiple statements into one. Try to create one SELECT query finding a batch of, let's say 1000 records. DB will return that 200 don't exist and you can go ahead and build one INSERT statement for those 200 queries.
Always perform measurements and always try to formulate what level of performance you are trying to achieve, to not make the code too verbose.
It's better to eliminate records that need to create.check records if not in the db then create
I'm indexing a MySQL table in ElasticSearch (full-text search). Instead of sending each new row at the time of its creation, we do a SQL query each N seconds (~30 seconds) for new records in that table. We do that by storing the last processed record ID (auto_increment) and issuing a query like:
SELECT * FROM myTable where id > lastProcessedId
My question: is this a good way to handle this? Are there any critical drawbacks? Are there any better alternatives?
We were also planning to use the same approach to handle users 'likes' (facebook style). Every N seconds we do a SQL query to get the latest 'likes' then process them and update each users' timeline.
We are trying to do it this way to avoid to mess with an old code base. But I'm not very comfortable with issuing this type of query each second, for example.
Any thoughts or problems with this solution?
Sounds expensive, I would consider other approaches.
Modify old code to index stuff on insert! I know it can be scary, but is it that bad? :)
Create an insert trigger that would somehow kick off an re-index process, i think you could have lots of options for how to build this.
Check out, http://www.roseindia.net/sql/trigger/mysql-trigger-after-insert.shtml
It's a little expensive, but frankly, if it's only every 30 seconds, I'd do it that way till it started to get painful.
There are other places you can put the data to pickup later and process, instead of staging via the database. You could use something as simple as appending a serialised copy to a file, writing a new one every 30-60 seconds, and then have a script process the previous unprocessed file(s). Similarly, you can put them into some other sort of queue, and then run that as often as you want.
I have an application where I receive each data 40.000 rows. I have 5 million rows to handle (500 Mb MySQL 5.0 database).
Actually, those rows are stored in the same table => slow to update, hard to backup, etc.
Which kind of scheme is used in such application to allow long term accessibility to the data without problems with too big tables, easy backup, fast read/write ?
Is postgresql better than mysql for such purpose ?
1 - 40000 rows / day is not that big
2 - Partition your data against the insert date : you can easily delete old data this way.
3 - Don't hesitate to go through a datamart step. (compute often asked metrics in intermediary tables)
FYI, I have used PostgreSQL with tables containing several GB of data without any problem (and without partitioning). INSERT/UPDATE time was constant
We're having log tables of 100-200million rows now, and it is quite painful.
backup is impossible, requires several days of down time.
purging old data is becoming too painful - it usually ties down the database for several hours
So far we've only seen these solutions:
backup , set up a MySQL slave. Backing up the slave doesn't impact the main db. (We havn't done this yet - as the logs we load and transform are from flat files - we back up these files and can regenerate the db in case of failures)
Purging old data, only painless way we've found is to introduce a new integer column that identifies the current date, and partition the tables(requires mysql 5.1) on that key, per day. Dropping old data is a matter of dropping a partition, which is fast.
If in addition you need to do continuously transactions on these tables(as opposed to just load data every now and then and mostly query that data), you probably need to look into InnoDB and not the default MyISAM tables.
The general answer is: you probably don't need all that detail around all the time.
For example, instead of keeping every sale in a giant Sales table, you create records in a DailySales table (one record per day), or even a group of tables (DailySalesByLocation = one record per location per day, DailySalesByProduct = one record per product per day, etc.)
First, huge data volumes are not always handled well in a relational database.
What some folks do is to put huge datasets in files. Plain old files. Fast to update, easy to back up.
The files are formatted so that the database bulk loader will work quickly.
Second, no one analyzes huge data volumes. They rarely summarize 5,000,000 rows. Usually, they want a subset.
So, you write simple file filters to cut out their subset, load that into a "data mart" and let them query that. You can build all the indexes they need. Views, everything.
This is one way to handle "Data Warehousing", which is that your problem sounds like.
First, make sure that your logging table is not over-indexed. By that i mean that every time you insert/update/delete from a table any indexes that you have also need to be updated which slows down the process. If you have a lot of indexes specified on your log table you should take a critical look at them and decide if they are indeed necessary. If not, drop them.
You should also consider an archiving procedure such that "old" log information is moved to a separate database at some arbitrary interval, say once a month or once a year. It all depends on how your logs are used.
This is the sort of thing that NoSQL DBs might be useful for, if you're not doing the sort of reporting that requires complicated joins.
CouchDB, MongoDB, and Riak are document-oriented databases; they don't have the heavyweight reporting features of SQL, but if you're storing a large log they might be the ticket, as they're simpler and can scale more readily than SQL DBs.
They're a little easier to get started with than Cassandra or HBase (different type of NoSQL), which you might also look into.
From this SO post:
http://carsonified.com/blog/dev/should-you-go-beyond-relational-databases/
I'm being given a data source weekly that I'm going to parse and put into a database. The data will not change much from week to week, but I should be updating the database on a regular basis. Besides this weekly update, the data is static.
For now rebuilding the entire database isn't a problem, but eventually this database will be live and people could be querying the database while I'm rebuilding it. The amount of data isn't small (couple hundred megabytes), so it won't load that instantaneously, and personally I want a bit more of a foolproof system than "I hope no one queries while the database is in disarray."
I've thought of a few different ways of solving this problem, and was wondering what the best method would be. Here's my ideas so far:
Instead of replacing entire tables, query for the difference between my current database and what I want to place in the database. This seems like it could be an unnecessary amount of work, though.
Creating dummy data tables, then doing a table rename (or having the server code point towards the new data tables).
Just telling users that the site is going through maintenance and put the system offline for a few minutes. (This is not preferable for obvious reasons, but if it's far and away the best answer I'm willing to accept that.)
Thoughts?
I can't speak for MySQL, but PostgreSQL has transactional DDL. This is a wonderful feature, and means that your second option, loading new data into a dummy table and then executing a table rename, should work great. If you want to replace the table foo with foo_new, you only have to load the new data into foo_new and run a script to do the rename. This script should execute in its own transaction, so if something about the rename goes bad, both foo and foo_new will be left untouched when it rolls back.
The main problem with that approach is that it can get a little messy to handle foreign keys from other tables that key on foo. But at least you're guaranteed that your data will remain consistent.
A better approach in the long term, I think, is just to perform the updates on the data directly (your first option). Once again, you can stick all the updating in a single transaction, so you're guaranteed all-or-nothing semantics. Even better would be online updates, just updating the data directly as new information becomes available. This may not be an option for you if you need the results of someone else's batch job, but if you can do it, it's the best option.
BEGIN;
DELETE FROM TABLE;
INSERT INTO TABLE;
COMMIT;
Users will see the changeover instantly when you hit commit. Any queries started before the commit will run on the old data, anything afterwards will run on the new data. The database will actually clear the old table once the last user is done with it. Because everything is "static" (you're the only one who ever changes it, and only once a week), you don't have to worry about any lock issues or timeouts. For MySQL, this depends on InnoDB. PostgreSQL does it, and SQL Server calls it "snapshotting," and I can't remember the details off the top of my head since I rarely use the thing.
If you Google "transaction isolation" + the name of whatever database you're using, you'll find appropriate information.
We solved this problem by using PostgreSQL's table inheritance/constraints mechanism.
You create a trigger that auto-creates sub-tables partitioned based on a date field.
This article was the source I used.
Which database server are you using? SQL 2005 and above provides a locking method called "Snapshot". It allows you to open a transaction, do all of your updates, and then commit, all while users of the database continue to view the pre-transaction data. Normally, your transaction would lock your tables and block their queries, but snapshot locking would be perfect in your case.
More info here: http://blogs.msdn.com/craigfr/archive/2007/05/16/serializable-vs-snapshot-isolation-level.aspx
But it requires SQL Server, so if you're using something else....
Several database systems (since you didn't specify yours, I'll keep this general) do offer the SQL:2003 Standard statement called MERGE which will basically allow you to
insert new rows into a target table from a source which don't exist there yet
update existing rows in the target table based on new values from the source
optionally even delete rows from the target that don't show up in the import table anymore
SQL Server 2008 is the first Microsoft offering to have this statement - check out more here, here or here.
Other database system probably will have similar implementations - it's a SQL:2003 Standard statement after all.
Marc
Use different table names(mytable_[yyyy]_[wk]) and a view for providing you with a constant name(mytable). Once a new table is completely imported update your view so that it uses that table.