I am switching to PostgreSQL from SQLite for a typical Rails application.
The problem is that running specs became slow with PG.
On SQLite it took ~34 seconds, on PG it's ~76 seconds which is more than 2x slower.
So now I want to apply some techniques to bring the performance of the specs on par with SQLite with no code modifications (ideally just by setting the connection options, which is probably not possible).
Couple of obvious things from top of my head are:
RAM Disk (good setup with RSpec on OSX would be good to see)
Unlogged tables (can it be applied on the whole database so I don't have change all the scripts?)
As you may have understood I don't care about reliability and the rest (the DB is just a throwaway thingy here).
I need to get the most out of the PG and make it as fast as it can possibly be.
Best answer would ideally describe the tricks for doing just that, setup and the drawbacks of those tricks.
UPDATE: fsync = off + full_page_writes = off only decreased time to ~65 seconds (~-16 secs). Good start, but far from the target of 34.
UPDATE 2: I tried to use RAM disk but the performance gain was within an error margin. So doesn't seem to be worth it.
UPDATE 3:*
I found the biggest bottleneck and now my specs run as fast as the SQLite ones.
The issue was the database cleanup that did the truncation. Apparently SQLite is way too fast there.
To "fix" it I open a transaction before each test and roll it back at the end.
Some numbers for ~700 tests.
Truncation: SQLite - 34s, PG - 76s.
Transaction: SQLite - 17s, PG - 18s.
2x speed increase for SQLite.
4x speed increase for PG.
First, always use the latest version of PostgreSQL. Performance improvements are always coming, so you're probably wasting your time if you're tuning an old version. For example, PostgreSQL 9.2 significantly improves the speed of TRUNCATE and of course adds index-only scans. Even minor releases should always be followed; see the version policy.
Don'ts
Do NOT put a tablespace on a RAMdisk or other non-durable storage.
If you lose a tablespace the whole database may be damaged and hard to use without significant work. There's very little advantage to this compared to just using UNLOGGED tables and having lots of RAM for cache anyway.
If you truly want a ramdisk based system, initdb a whole new cluster on the ramdisk by initdbing a new PostgreSQL instance on the ramdisk, so you have a completely disposable PostgreSQL instance.
PostgreSQL server configuration
When testing, you can configure your server for non-durable but faster operation.
This is one of the only acceptable uses for the fsync=off setting in PostgreSQL. This setting pretty much tells PostgreSQL not to bother with ordered writes or any of that other nasty data-integrity-protection and crash-safety stuff, giving it permission to totally trash your data if you lose power or have an OS crash.
Needless to say, you should never enable fsync=off in production unless you're using Pg as a temporary database for data you can re-generate from elsewhere. If and only if you're doing to turn fsync off can also turn full_page_writes off, as it no longer does any good then. Beware that fsync=off and full_page_writes apply at the cluster level, so they affect all databases in your PostgreSQL instance.
For production use you can possibly use synchronous_commit=off and set a commit_delay, as you'll get many of the same benefits as fsync=off without the giant data corruption risk. You do have a small window of loss of recent data if you enable async commit - but that's it.
If you have the option of slightly altering the DDL, you can also use UNLOGGED tables in Pg 9.1+ to completely avoid WAL logging and gain a real speed boost at the cost of the tables getting erased if the server crashes. There is no configuration option to make all tables unlogged, it must be set during CREATE TABLE. In addition to being good for testing this is handy if you have tables full of generated or unimportant data in a database that otherwise contains stuff you need to be safe.
Check your logs and see if you're getting warnings about too many checkpoints. If you are, you should increase your checkpoint_segments. You may also want to tune your checkpoint_completion_target to smooth writes out.
Tune shared_buffers to fit your workload. This is OS-dependent, depends on what else is going on with your machine, and requires some trial and error. The defaults are extremely conservative. You may need to increase the OS's maximum shared memory limit if you increase shared_buffers on PostgreSQL 9.2 and below; 9.3 and above changed how they use shared memory to avoid that.
If you're using a just a couple of connections that do lots of work, increase work_mem to give them more RAM to play with for sorts etc. Beware that too high a work_mem setting can cause out-of-memory problems because it's per-sort not per-connection so one query can have many nested sorts. You only really have to increase work_mem if you can see sorts spilling to disk in EXPLAIN or logged with the log_temp_files setting (recommended), but a higher value may also let Pg pick smarter plans.
As said by another poster here it's wise to put the xlog and the main tables/indexes on separate HDDs if possible. Separate partitions is pretty pointless, you really want separate drives. This separation has much less benefit if you're running with fsync=off and almost none if you're using UNLOGGED tables.
Finally, tune your queries. Make sure that your random_page_cost and seq_page_cost reflect your system's performance, ensure your effective_cache_size is correct, etc. Use EXPLAIN (BUFFERS, ANALYZE) to examine individual query plans, and turn the auto_explain module on to report all slow queries. You can often improve query performance dramatically just by creating an appropriate index or tweaking the cost parameters.
AFAIK there's no way to set an entire database or cluster as UNLOGGED. It'd be interesting to be able to do so. Consider asking on the PostgreSQL mailing list.
Host OS tuning
There's some tuning you can do at the operating system level, too. The main thing you might want to do is convince the operating system not to flush writes to disk aggressively, since you really don't care when/if they make it to disk.
In Linux you can control this with the virtual memory subsystem's dirty_* settings, like dirty_writeback_centisecs.
The only issue with tuning writeback settings to be too slack is that a flush by some other program may cause all PostgreSQL's accumulated buffers to be flushed too, causing big stalls while everything blocks on writes. You may be able to alleviate this by running PostgreSQL on a different file system, but some flushes may be device-level or whole-host-level not filesystem-level, so you can't rely on that.
This tuning really requires playing around with the settings to see what works best for your workload.
On newer kernels, you may wish to ensure that vm.zone_reclaim_mode is set to zero, as it can cause severe performance issues with NUMA systems (most systems these days) due to interactions with how PostgreSQL manages shared_buffers.
Query and workload tuning
These are things that DO require code changes; they may not suit you. Some are things you might be able to apply.
If you're not batching work into larger transactions, start. Lots of small transactions are expensive, so you should batch stuff whenever it's possible and practical to do so. If you're using async commit this is less important, but still highly recommended.
Whenever possible use temporary tables. They don't generate WAL traffic, so they're lots faster for inserts and updates. Sometimes it's worth slurping a bunch of data into a temp table, manipulating it however you need to, then doing an INSERT INTO ... SELECT ... to copy it to the final table. Note that temporary tables are per-session; if your session ends or you lose your connection then the temp table goes away, and no other connection can see the contents of a session's temp table(s).
If you're using PostgreSQL 9.1 or newer you can use UNLOGGED tables for data you can afford to lose, like session state. These are visible across different sessions and preserved between connections. They get truncated if the server shuts down uncleanly so they can't be used for anything you can't re-create, but they're great for caches, materialized views, state tables, etc.
In general, don't DELETE FROM blah;. Use TRUNCATE TABLE blah; instead; it's a lot quicker when you're dumping all rows in a table. Truncate many tables in one TRUNCATE call if you can. There's a caveat if you're doing lots of TRUNCATES of small tables over and over again, though; see: Postgresql Truncation speed
If you don't have indexes on foreign keys, DELETEs involving the primary keys referenced by those foreign keys will be horribly slow. Make sure to create such indexes if you ever expect to DELETE from the referenced table(s). Indexes are not required for TRUNCATE.
Don't create indexes you don't need. Each index has a maintenance cost. Try to use a minimal set of indexes and let bitmap index scans combine them rather than maintaining too many huge, expensive multi-column indexes. Where indexes are required, try to populate the table first, then create indexes at the end.
Hardware
Having enough RAM to hold the entire database is a huge win if you can manage it.
If you don't have enough RAM, the faster storage you can get the better. Even a cheap SSD makes a massive difference over spinning rust. Don't trust cheap SSDs for production though, they're often not crashsafe and might eat your data.
Learning
Greg Smith's book, PostgreSQL 9.0 High Performance remains relevant despite referring to a somewhat older version. It should be a useful reference.
Join the PostgreSQL general mailing list and follow it.
Reading:
Tuning your PostgreSQL server - PostgreSQL wiki
Number of database connections - PostgreSQL wiki
Use different disk layout:
different disk for $PGDATA
different disk for $PGDATA/pg_xlog
different disk for tem files (per database $PGDATA/base//pgsql_tmp) (see note about work_mem)
postgresql.conf tweaks:
shared_memory: 30% of available RAM but not more than 6 to 8GB. It seems to be better to have less shared memory (2GB - 4GB) for write intensive workloads
work_mem: mostly for select queries with sorts/aggregations. This is per connection setting and query can allocate that value multiple times. If data can't fit then disk is used (pgsql_tmp). Check "explain analyze" to see how much memory do you need
fsync and synchronous_commit: Default values are safe but If you can tolerate data lost then you can turn then off
random_page_cost: if you have SSD or fast RAID array you can lower this to 2.0 (RAID) or even lower (1.1) for SSD
checkpoint_segments: you can go higher 32 or 64 and change checkpoint_completion_target to 0.9. Lower value allows faster after-crash recovery
Related
Me got 15 x 100 million 32-byte records. Only sequential access and appends needed. The key is a Long. The value is a tuple - (Date, Double, Double). Is there something in this universe which can do this? I am willing to have 15 seperate databases (sql/nosql) or files for each of those 100 million records. I only have a i7 core and 8 GB RAM and 2 TB hard disk.
I have tried PostgreSQL, MySQL, Kyoto Cabinet (with fine tuning) with Protostuff encoding.
SQL DBs (with indices) take forever to do the silliest query.
Kyoto Cabinet's B-Tree can handle upto 15-18 million records beyond which appends take forever.
I am fed up so much that I am thinking of falling back on awk + CSV which I remember used to work for this type of data.
If you scenario means always going through all records in sequence then it may be an overkill to use a database. If you start to need random lookups, replacing/deleting records or checking if a new record is not a duplicate of an older one, a database engine would make more sense.
For the sequential access, a couple of text files or hand-crafted binary files will be easier to handle. You sound like a developer - I would probably go for an own binary format and access it with help of memory-mapped files to improve the sequential read/append speed. No caching, just a sliding window to read the data. I think that it would perform better and even on usual hardware than any DB would; I did such data analysis once. It would also be faster than awking CSV files; however, I am not sure how much and if it satisfied the effort to develop the binary storage, first of all.
As soon as the database becomes interesting, you can have a look at MongoDB and CouchDB. They are used for storing and serving very large amounts of data. (There is a flattering evaluation that compares one of them to traditional DBs.). Databases usually need a reasonable hardware power to perform better; maybe you could check out how those two would do with your data.
--- Ferda
Ferdinand Prantl's answer is very good. Two points:
By your requirements I recommend that you create a very tight binary format. This will be easy to do because your records are fixed size.
If you understand your data well you might be able to compress it. For example, if your key is an increasing log value you don't need to store it entirely. Instead, store the difference to the previous value (which is almost always going to be one). Then, use a standard compression algorithm/library to save on data size big time.
For sequential reads and writes, leveldb will handle your dataset pretty well.
I think that's about 48 gigs of data in one table.
When you get into large databases, you have to look at things a little differently. With an ordinary database (say, tables less than a couple million rows), you can do just about anything as a proof of concept. Even if you're stone ignorant about SQL databases, server tuning, and hardware tuning, the answer you come up with will probably be right. (Although sometimes you might be right for the wrong reason.)
That's not usually the case for large databases.
Unfortunately, you can't just throw 1.5 billion rows straight at an untuned PostgreSQL server, run a couple of queries, and say, "PostgreSQL can't handle this." Most SQL dbms have ways of dealing with lots of data, and most people don't know that much about them.
Here are some of the things that I have to think about when I have to process a lot of data over the long term. (Short-term or one-off processing, it's usually not worth caring a lot about speed. A lot of companies won't invest in more RAM or a dozen high-speed disks--or even a couple of SSDs--for even a long-term solution, let alone a one-time job.)
Server CPU.
Server RAM.
Server disks.
RAID configuration. (RAID 3 might be worth looking at for you.)
Choice of operating system. (64-bit vs 32-bit, BSD v. AT&T derivatives)
Choice of DBMS. (Oracle will usually outperform PostgreSQL, but it costs.)
DBMS tuning. (Shared buffers, sort memory, cache size, etc.)
Choice of index and clustering. (Lots of different kinds nowadays.)
Normalization. (You'd be surprised how often 5NF outperforms lower NFs. Ditto for natural keys.)
Tablespaces. (Maybe putting an index on its own SSD.)
Partitioning.
I'm sure there are others, but I haven't had coffee yet.
But the point is that you can't determine whether, say, PostgreSQL can handle a 48 gig table unless you've accounted for the effect of all those optimizations. With large databases, you come to rely on the cumulative effect of small improvements. You have to do a lot of testing before you can defensibly conclude that a given dbms can't handle a 48 gig table.
Now, whether you can implement those optimizations is a different question--most companies won't invest in a new 64-bit server running Oracle and a dozen of the newest "I'm the fastest hard disk" hard drives to solve your problem.
But someone is going to pay either for optimal hardware and software, for dba tuning expertise, or for programmer time and waiting on suboptimal hardware. I've seen problems like this take months to solve. If it's going to take months, money on hardware is probably a wise investment.
I have a simpleton question on Redis. If the key to it's performance is that it's in-memory, whey can't that be done on a regular SQL db?
Any DBMS can be run "in memory". Consider the use of a ramdisk. However, most DBMSs (those with SQL) are not designed to run entirely in memory and put alot of effort to minimize disk IO and paging: a DBMS works very hard to keep the "relevant data" hot (in memory and in cache) -- IO is slow, slow slow.
This is because database data is often [and has historically been] significantly larger than main memory. That and main-memory is volatile :-) [ACID DBMSs do lots of works with write-ahead logging -- to a non-volatile store -- and other techniques to ensure data is never corrupted, even in case of a unexpected shutdown.]
Some databases, like SQLite use the same format for the disk and memory stores even though they explicitly support an in-memory store. Support for other [in-memory] back-ends and memory usage tuning vary by provider.
Happy coding.
You may be interested in VoltDB
The key is not only is it in memory, but it also has simpler operations than a SQL DB. Redis has simple operations such as GET, SET (and so on) using hash tables, and other optimized data structures.
SQL Databases generally take longer to compute, however they are a ton more flexible and in most cases more powerful (in terms of what type of queries). You most certainly cannot run JOIN queries in Redis, for example
You may be interested in TimesTen (which is now Oracle).
In 11g its SQL has improved significantly, though still is not as powerful as that of Oracle.
You can do that natively with some SQL database management systems. But there are risks.
You stand to lose data if the server fails, for example. I don't think you can get ACID compliant transactions; any log file would have to be written to disk to survive a server failure. (I imagine it's possible for an in-memory SQL dbms to still write log files to disk, but I've never run across that myself. Not that I've looked much.)
On DB's in RAM: Traditional databases will eventually wind up in RAM:
Traditional database data — records of human transactional activity [...] — will not grow as fast as Moore’s Law makes computer chips cheaper.
And that point has a straightforward corollary, namely:
It will become ever more affordable to put traditional database data entirely into RAM.
Consider this scenario with the following assumptions:
The database is used for a non-critical webapp.
Query speed is of vital importance.
The read/write patterns are roughly >95 % reads and <5 % writes.
The database is backuped up daily using mysqldump.
The is no need for transactions or advanced crash recovery. If the database crashes I'll simply import last night's mysqldump. This is good enough in this scenario.
No need for full-text searching.
Pros of MyISAM under said assumptions:
It's very fast (with one exception - see below).
It's light-weight and has an easy-to-understand mapping between database/table to physical files in file system (.MYD/.MYI/.frm).
Easy backup (mysqldump).
All-in-all I'm very happy with MyISAM with one major exception. MyISAM has one major shortcoming under said assumptions and that is table level locking. When UPDATEs are running towards a frequently read table all reads are blocked. Needless to say this causes major performance problems which must be solved.
My questions are:
Is there some way to get rid of table level locking without switching away from MyISAM?
If I must switch to InnoDB - how do I configure InnoDB so that it behaves as similar to MyISAM as possible (think no transactions, logical file structure, etc.). How do I configure a InnoDB to be "just like MyISAM but without table level locking"?
No, MyISAM means table level locking.
You can't get it "just like", but you can get it "a lot more like" by turning on the innodb_file_per_table option. InnoDB will still store critical information in its system-level data file, however, and you can't do things like casually rename a database by renaming the directory it lives in like you can with MyISAM.
Have you actually taken performance metrics using myisam and innodb tables? In my experience the differences in speed is not really that much when you consider all the ACID benefits you get from innodb. Just the table locking alone will affect speed such that innodb would be overall faster.
Also notice that myisam is much faster on inserts, not so much on selects. You are inserting only 5% of the time... do the math.
You can always do mysqldump using an innodb, so your backup rocess is the same.
I know some projects use a mirror DB for searching. It tends to be optimized for the searches and sometimes even run on a different machine, just to isolate the overhead.
The only drawback here is that keeping them in sync is a bit of a hassle. If stale data in your search table isn't too troubling, it might be the best bet. If performance is an issue that is.
It isn't my favorite solution, but it is pretty simple in theory.
When it comes to backup InnoDB doesn't prevent you from using mysqldump.
Are you sure that you really need to maintain the mapping between database tables and files on disk? Manual operations on database files are rarely a good idea.
With InnoDB you don't have to use transactions, by default it works in "autocommit" mode (every query will be commited automatically).
"InnoDB is slower" is mostly myth these days, but of course it depends on your workload.
In other words I think you should definitely give InnoDB a try and benchmark the performance of your application. Migration is extremely simple, so I don't see a reason not to try. For me InnoDB is a default choice for a long time.
How does the work_mem option in Postgres work? Here's the description from http://www.postgresql.org/docs/8.4/static/runtime-config-resource.html:
Specifies the amount of memory to be used by internal
sort operations and hash tables before switching to
temporary disk files. The value defaults to one megabyte
(1MB). Note that for a complex query, several sort or
hash operations might be running in parallel; each one
will be allowed to use as much memory as this value
specifies before it starts to put data into temporary
files. Also, several running sessions could be doing
such operations concurrently. So the total memory used
could be many times the value of work_mem; it is
necessary to keep this fact in mind when choosing the
value. Sort operations are used for ORDER BY, DISTINCT,
and merge joins. Hash tables are used in hash joins,
hash-based aggregation, and hash-based processing of IN
subqueries.
I'm probably totally wrong here but..isn't "switching to temporary disk files" essentially the same thing as "virtual memory" in the operating system? Wouldn't the OS just create a swap file once the RAM is gone? Wouldn't it be better to set this to something like 100TB and let the OS figure it out? Before I potentially mess up my system, I want to check if anyone actually tried this approach.
PostgreSQL will for example switch to a sorting operation more suitable for on-disk sort than in-memory sort if it knows the sort will happen on disk - which it won't know if it happens in swap.
Also, PostgreSQL can switch to a completely different plan (for example, using a different JOIN method) if it figures out the data does not fit in RAM.
Setting work_mem too high will get you a very slow database as soon as you have enough data so that everything doesn't always fit in RAM anymore.
Keep in mind that work_mem is the maximum amount of RAM that can be used for every single sort operation. For a single query, multiple sort operations might run in parallel and there might be multiple connections querying the database at once. For that reason all sort operations may use x-times the amount of work_mem in RAM (that's the reason a conservative amount is recommended).
Now back to your question, if you choose a work_mem to a such high value, sort operations might use up most of your RAM, which leads to page in and out's from swap (keep in mind, there are lots of other processes and PostgreSQL parts that need some (or even lots of) RAM. Disk-based sort operations are by factors more efficient than page swaps done by the OS. As some of the other replies pointed out, a database server which has swap out and in constantly will perform extremely slow.
Another point is, that with such a high work_mem value, a single query (purposely or by accident) might more or less make the whole database server go unresponsive.
A database server that swaps is a dead database server.
In RAM postgres uses quicksort, on disk it uses another algorithm which is much more suited to harddisks. Using quicksort on swapped-out memory will be incredibly slow.
The OS is generic in the terms it handles swap, besides, there's a finite amount of address space a process can use, which isn't that big on 32 bit systems(2Gb on a windows 32 bit platform, can be enhanced to 3Gb), but you're right, you could let the OS handle this through virtual memory.
PostgreSQL is not 'generic' it'll know much better than the OS how to structure data once disk access is involved, so letting the database switch over to explicit file handling once memory is exhausted will have benefits over letting the OS handle it.
Keep in mind that I am a rookie in the world of sql/databases.
I am inserting/updating thousands of objects every second. Those objects are actively being queried for at multiple second intervals.
What are some basic things I should do to performance tune my (postgres) database?
It's a broad topic, so here's lots of stuff for you to read up on.
EXPLAIN and EXPLAIN ANALYZE is extremely useful for understanding what's going on in your db-engine
Make sure relevant columns are indexed
Make sure irrelevant columns are not indexed (insert/update-performance can go down the drain if too many indexes must be updated)
Make sure your postgres.conf is tuned properly
Know what work_mem is, and how it affects your queries (mostly useful for larger queries)
Make sure your database is properly normalized
VACUUM for clearing out old data
ANALYZE for updating statistics (statistics target for amount of statistics)
Persistent connections (you could use a connection manager like pgpool or pgbouncer)
Understand how queries are constructed (joins, sub-selects, cursors)
Caching of data (i.e. memcached) is an option
And when you've exhausted those options: add more memory, faster disk-subsystem etc. Hardware matters, especially on larger datasets.
And of course, read all the other threads on postgres/databases. :)
First and foremost, read the official manual's Performance Tips.
Running EXPLAIN on all your queries and understanding its output will let you know if your queries are as fast as they could be, and if you should be adding indexes.
Once you've done that, I'd suggest reading over the Server Configuration part of the manual. There are many options which can be fine-tuned to further enhance performance. Make sure to understand the options you're setting though, since they could just as easily hinder performance if they're set incorrectly.
Remember that every time you change a query or an option, test and benchmark so that you know the effects of each change.
Actually there are some simple rules which will get you in most cases enough performance:
Indices are the first part. Primary keys are automatically indexed. I recommend to put indices on all foreign keys. Further put indices on all columns which are frequently queried, if there are heavily used queries on a table where more than one column is queried, put an index on those columns together.
Memory settings in your postgresql installation. Set following parameters higher:
.
shared_buffers, work_mem, maintenance_work_mem, temp_buffers
If it is a dedicated database machine you can easily set the first 3 of these to half the ram (just be carefull under linux with shared buffers, maybe you have to adjust the shmmax parameter), in any other cases it depends on how much ram you would like to give to postgresql.
http://www.postgresql.org/docs/8.3/interactive/runtime-config-resource.html
http://wiki.postgresql.org/wiki/Performance_Optimization
The absolute minimum I'll recommend is the EXPLAIN ANALYZE command. It will show a breakdown of subqueries, joins, et al., all the time showing the actual amount of time consumed in the operation. It will also alert you to sequential scans and other nasty trouble.
It is the best way to start.
Put fsync = off in your posgresql.conf, if you trust your filesystem, otherwise each postgresql operation will be imediately written to the disk (with fsync system call).
We have this option turned off on many production servers since quite 10 years, and we never had data corruptions.