I have a database efficiency question.
Here is some info about my table:
-table of about 500-1000 records
-records are added and deleted every day.
- usually have about the same amount being added and deleted every day (size of active records stays the same)
Now, my question is.....when I delete records,...should I (A) delete the record and move it to a new table?
Or,...should I (B) just have and "active" column and set the record to 0 when it is no long active.
The reason I am hesitant to use B is because my site is based on the user being able to filter/sort this table of 500-1000 records on the fly (using ajax)....so I need it to be as fast as possible,..(i'm guessing a table with more records would be slower to filter)...and I am using mySQL InnoDB.
Any input would be great, Thanks
Andrew
~1000 records is a very small number.
If a record can be deleted and re-added later, maybe it makes sense to have an "active" indicator.
Realistically, this isn't a question about DB efficiency but about network latency and the amount of data you're sending over the wire. As far as MySQL goes, 1000 rows or 100k rows are going to be lightning-fast, so that's not a problem.
However, if you've got a substantial amount of data in those rows, and you're transmitting it all to the client through AJAX for filtering, the network latency is your bottleneck. If you're transmitting a handful of bytes (say 20) per row and your table stays around 1000 records in length, not a huge problem.
On the other hand, if your table grows (with inactive records) to, say, 20k rows, now you're transmitting 400k instead of 20k. Your users will notice. If the records are larger, the problem will be more severe as the table grows.
You should really do the filtering on the server side. Let MySQL spend 2ms filtering your table before you spend a full second or two sending it through Ajax.
It depends on what you are filtering/sorting on and how the table is indexed.
A third, and not uncommon, option, you could have a hybrid approach where you inactivate records (B) (optionally with a timestamp) and periodically archive them to a separate table (A) (either en masse or based on the timestamp age).
Realistically, if your table is in the order 1000 rows, it's probably not worth fussing too much over it (assuming the scalability of other factors is known).
If you need to keep the records for some future purpose, I would set an Inactive bit.
As long as you have a primary key on the table, performance should be excellent when SELECTing the records.
Also, if you do the filtering/sorting on the client-side then the records would only have to be retrieved once.
Related
Data is increasing in one table everyday, it might lower the performance . I was thinking if I can create a trigger which move table A into A1 and create a new table A every a period of time, so that insert or update could be faster in table A. Is this the right way to save performance ? If not, what should I do ?
(for example, insert or update 1000 rows per second in table A, how is the performance after 3 years ?)
We are designing softwares for a factory. There are product lines which pcb boards are made on. We need to insert almost 60 pcb records per second for years. (1000 rows seem to be exaggerated)
First, you are talking about several terabytes for a single table. Is your disk that big? Yes, MySQL can handle that big a table.
Will it slow down? It depends on
The indexes. If you have 'random' indexes, the INSERTs will slow down to about 1 insert per disk hit. On a spinning HDD, that is only about 100 per second. SSD might be able to handle 1000/sec. Please provide SHOW CREATE TABLE.
Does the table have an AUTO_INCREMENT? If so, it needs to be BIGINT, not INT. But, if possible, get rid of it all together (to save space). Again, let's see the SHOW.
"Point" queries (load one row via an index) are mostly unaffected by the size of the table. They will be about twice as slow in a trillion-row table as in a million-row table. A point query will take milliseconds or tens of milliseconds; no big deal.
A table scan will take hours or days; hopefully you are not doing that.
A billion-row scan of part of the table will take days or weeks unless you are using the PRIMARY KEY or have a "covering" index. Let's see the queries and the SHOW.
The best technique is not to store the data. Summarize it as it arrives, save the summaries, then toss the raw data. (OK, you might store the raw in a csv file just in case you need to build a new summary table or fix a bug in an existing one.)
Having a few summary tables instead of the raw data would shrink the data to under 1TB and allow the relevant queries to run 10 times as fast. (OK, point queries would be only slightly faster.)
PARTITIONing (or otherwise splitting up the table)? It depends. Let's see the queries and the SHOW. In many situations, PARTITIONing does not speed up anything.
Will you be deleting or modifying existing rows? I hope not. That adds more dimensions of problems. If, on the other hand, you need to purge 'old' data, then that is an excellent use for PARTITIONing. For 3 years' worth of data, I would PARTITION BY RANGE(TO_DAYS(..)) and have monthly partitions. Then a monthly DROP PARTITION would be very fast.
Very Huge data may decrease the performance of server, So there is a way to handle this :
1) you have to create another table to store archive data ( old data ) using Archive storage mechanism . ( https://dev.mysql.com/doc/refman/8.0/en/archive-storage-engine.html )
2) create MySQL job/scheduler to move older records to archive table. schedule in timeslot
when server is maximum idle.
3) after moving older records to archive table, re-index the original table.
this will serve the purpose of performance.
It is unlikely that 1000 row tables perform sufficiently poorly that doing a table copy every once in a while is an overall net gain. And anyway, what would the new table have that the old one did not which would improve performance?
The key to having tables perform efficiently is intelligent table design and management of indexes. That is how zillion row tables are effective in geospatial work, library catalogs, astronomy, and how internet search engines find useful data, etc.
Each index defined does cause more mysql impact especially at row insert time. Assuming there are more reads than inserts, this is an advantage because most queries are rapidly completed thanks to a suitable index.
Indexes are best defined with a thorough understanding of the queries made against the table—both in quality and quantity. And, if there is any tendency for the nature of the queries to trend over months or years, then the indexes would need additions, modifications, or—yes—even deletions.
It seems to me there is something inherently wrong with the way you are using MySQL to begin with.
A database system is supposed to manage data that is required by your application in order for it to work. If you think flushing the table every so often is something acceptable, then that doesn't seem to be the case.
Perhaps you are better off just using log files. Split them by date, delete old ones if and when you decide they are no longer relevant or need the disk space. It's even safer to do that way from a recovery perspective.
If you need a better suggestion, then improve your question to include exactly what you are trying to accomplish so we can help you with it.
I have 3 very large tables with clustered indexes on composite keys. No updates only inserts. New inserts will not be within the existing index range but the new inserts will not align with the clustered index and these tables get a lot of inserts (hundreds - thousands per second). What would like to do is DBREINDEX with Fill Factor = 100 but then set a Fill Factor of 5 and have that Fill Factor ONLY applied to inserts. Right now a Fill Factor applies to the whole table only. Is there a way to have a Fill Factor that applies to inserts (or inserts and updates) only? I don't care about select speed at this time. I am loading data. When the data load is complete then I will DBREINDEX at 100. A Fill Factor of 10 versus 30 doubles the rates at which new data is inserted. This load will takes a couple days and it cannot go live until the data is loaded. The clustered indexes are aligned with dominate query used by the end user application.
My practice is to DBREINDEX daily but the problem is now that the tables are getting large a 10 DBREINDEX takes a long time. I have considered indexing into "daily" tables and then inserting that data daily sorted by the clustered index into the production tables.
If you read this far even more. The indexes are all composite and I am running 6 instances of the parser on an 8 core server (lot of testing and that seems to have the best throughput). The data out of a SINGLE parser is in PK order and I am doing the inserts 990 values at a time (SQL value limits). The 3 active tables only share data via a foreign key relationship with a single relative inactive 4th table. My thought at this time is to have holding tables for each parser and then have another process that polls those table for the next complete insert and move the data into the production table in PK order. That is going to be a lot of work. I hope someone has a better idea.
The parses start in PK order but rarely finish in PK order. Some individual parses are so large that I could not hold all the data in memory until the end. Right now the SQL insert is slightly faster than the parse that creates the data. In an individual parse I run the insert asynch and go on parsing but don't insert until the prior insert is complete.
I agree you should have holding tables for the parser data and only insert to the main tables when you're ready. I implemented something similar in a former life (it was quasi-hashed into 10 tables based on mod 10 of the unique ID, then rolled into the primary table later - primarily to assist in load speed). If you're going to use holding tables then I see no need to have them at anything but FF = 100. The less pages you have to use the better.
Apparently, too, you should test the difference permanent tables, #temp tables and table-valued parameters. :-)
I have a table where when a row is created, it will be active for 24 hours with some writes and lots of reads. Then it becomes inactive after 24 hours and will have no more writes and only some reads, if any.
Is it better to keep these rows in the table or move them when they become inactive (or via batch jobs) to a separate table? Thinking in terms of performance.
This depends largely on how big your table will get, but if it grows forever, and has a significant number of rows per day, then there is a good chance that moving old data to another table would be a good idea. There are a few different ways you could accomplish this, and which is best depends on your application and data access patterns.
Essentially as you said, when a row becomes "old", INSERT to the archive table, and DELETE from the current table.
Create a new table every day (or perhaps every week, or every month, depending on how big your dataset is), and never worry about moving old rows. You'll just have to query old tables when accessing old data, but for the current day, you only ever access the current table.
Have a "today" table and a "all time" table. Duplicate the "today" rows in both tables, keeping them in sync with triggers or other mechanisms. When a row becomes old, simply delete from the "today" table, leaving the "all time" row in tact.
One advantage to #2, that may not be immediately obvious, is that I believe MySQL indexes can be optimized for read-only tables. So by having old tables that are never written to, you can take advantage of this extra optimization.
Generally moving rows between tables in proper RDBMS should not be necessary.
I'm not familiar with mysql specifics, but you should do fine with the following:
Make sure your timestamp column is indexed
In addition, you can use active BOOLEAN default true column
Make a batch run every day to mark >24h old rows inactive
Use a partial index for timestamp column so only rows marked active are indexed
Remember to have timestamp and active = TRUE in your where conditions to hit indexes. Use EXPLAIN a lot.
That all depends on the balance between ease of programming, and performance. Performance wise, yes it will definitely be faster. But whether the speed increase is worth the effort is hard to say.
I've worked on systems that run perfectly fine with millions of rows. However, if the data is ever growing it does eventually become a problem.
I've worked on a database storing transaction logging for automated equipment. It generates hundreds of thousands of events per day. After a year, the queries just wouldn't run at acceptable speeds any more. We now keep the last month's worth of logs in the main table (millions of rows still), and move older data to archive tables.
None of the application's functionality ever looks in the archive table (if you do a query of the transaction log, it will return no results). It is only really kept for emergency use, and is just queried with any standalone database query tool. Because the archive has well over a hundred million rows, and the nature of this emergency use is generally unplannable (and therefore mostly un-indexed) queries, they can take a long time to run.
There is another solution. To have another table containing only the active records (tblactiverecords). When the number of active records is really small, you could just do an inner join and get the active records. This should take very less time because primary key by default are indexed in mysql. As your rows become inactive, you could delete them from the tblactiverecords table.
create table tblrecords (id int primary key, data text);
Then,
create table tblactiverecords (tblrecords_id primary key);
you can do
select data from tblrecords join tblactiverecords on tblrecords.id = tblactiverecords.tblrecords_id;
to get all data that are active.
Recently I've learned the wonder of indexes, and performance has improved dramatically. However, with all I've learned, I can't seem to find the answer to this question.
Indexes are great, but why couldn't someone just index all fields to make the table incredibly fast? I'm sure there's a good reason to not do this, but how about three fields in a thirty-field table? 10 in a 30 field? Where should one draw the line, and why?
Indexes take up space in memory (RAM); Too many or too large of indexes and the DB is going to have to be swapping them to and from the disk. They also increase insert and delete time (each index must be updated for every piece of data inserted/deleted/updated).
You don't have infinite memory. Making it so all indexes fit in RAM = good.
You don't have infinite time. Indexing only the columns you need indexed minimizes the insert/delete/update performance hit.
Keep in mind that every index must be updated any time a row is updated, inserted, or deleted. So the more indexes you have, the slower performance you'll have for write operations.
Also, every index takes up further disk space and memory space (when called), so it could potentially slow read operations as well (for large tables).
Check this out
You have to balance CRUD needs. Writing to tables becomes slow. As for where to draw the line, that depends on how the data is being acessed (sorting filtering, etc.).
Indexing will take up more allocated space both from drive and ram, but also improving the performance a lot. Unfortunately when it reaches memory limit, the system will surrender the drive space and risk the performance. Practically, you shouldn't index any field that you might think doesn't involve in any kind of data traversing algorithm, neither inserting nor searching (WHERE clause). But you should if otherwise. By default you have to index all fields. The fields which you should consider unindexing is if the queries are used only by moderator, unless if they need for speed too
It is not a good idea to indexes all the columns in a table. While this will make the table very fast to read from, it also becomes much slower to write to. Writing to a table that has every column indexed would involve putting the new record in that table and then putting each column's information in the its own index table.
this answer is my personal opinion based I m using my mathematical logic to answer
the second question was about the border where to stop, First let do some mathematical calculation, suppose we have N rows with L fields in a table if we index all the fields we will get a L new index tables where every table will sort in a meaningfull way the data of the index field, in first glance if your table is a W weight it will become W*2 (1 tera will become 2 tera) if you have 100 big table (I already worked in project where the table number was arround 1800 table ) you will waste 100 times this space (100 tera), this is way far from wise.
If we will apply indexes in all tables we will have to think about index updates were one update trigger all indexes update this is a select all unordered equivalent in time
from this I conclude that you have in this scenario that if you will loose this time is preferable to lose it in a select nor an update because if you will select a field that is not indexed you will not trigger another select on all fields that are not indexed
what to index ?
foreign-keys : is a must based on
primary-key : I m not yet sure about it may be if someone read this could help on this case
other fields : the first natural answer is the half of the remaining filds why : if you should index more you r not far from the best answer if you should index less you are not also far because we know that no index is bad and all indexed is also bad.
from this 3 points I can conclude that if we have L fields composed of K keys the limit should be somewhere near ((L-K)/2)+K more or less by L/10
this answer is based on my logic and personal prictices
First of all, at least in SAP - ABAP and in background database table, we can create one index table for all required index fields, we will have their addresses only. So other SQL related software-database system can also use one table for all fields to be indexed.
Secondly, what is the writing performance? A company in one day records 50 sales orders for example. And let assume there is a table VBAK sales order header table with 30 fields for example each has 20 CHAR length..
I can write to real table in seconds, but other index table can work in the background, and at the same time a report is tried to be run, for this report while index table is searched, ther can be a logic- for database programming- a index writing process is contiuning and wait it for ending ( 5 sales orders at the same time were being recorded for example and take maybe 5 seconds) ..so , a running report can wait 5 seconds then runs 5 seconds total 10 seconds..
without index, a running report does not wait 5 seconds for writing performance..but runs maybe 40 seconds...
So, what is the meaning of writing performance no one writes thousands of records at the same time. But reading them.
And reading a second table means that : there were all ready sorted fields.I have 3 fields selected and I can find in which sorted sets I need to search these data, then I bring them...what RAM, what memory it is just a copied index table with only one data for each field -address data..What memory?
I think, this is one of the software company secrets hide from customers, not to wake them up , otherwise they will not need another system in the future with an expensive price.
I have a table in MySQL dB that records when a person clicks on certain navigation tabs. Each time it will soft-delete the last entry and insert a new one. The reason for soft-delete is for analytics purposes, so I can track over time where/when/what users are clicking. The ratio of soft-deletes to new entries are 9:1, and the table size is about 20K at the moment but growing fast.
So my question is: if deleting the soft-delete entries would help optimize any queries that involve this table? There is one at the moment that joins 4 tables together and only needs the new entries. Since the analytics on the soft-deletes could be performed on backup copies, I don't need these rows on the production dB.
There is most likely a performance implication to have 90% of your table excluded out of all queries but the analytics: your indexes are probably bigger than they would be if these soft-deleted rows were in their own table, the disk head has to seek across bigger distances so your disk accesses are more expensive than they would be in table 1/10 the size etc.