Consider the following scenario:
A database is to be used on a local network, to which a handful of clients connect at one time. There is one table. When one of the clients modifies the data in that table, the other connected clients should be notified. Perhaps there could be one thread in the server application per connection.
One constraint is that should not require the user to install and configure any other third-party database software. This is why Sqlite came to mind, since the application itself could just interact with the .db file, which can be bundled with it.
Is this something that is achievable with Sqlite, or is this idea totally wrong and misguided?
Simple diagram illustrating this description.
Summary of the exchange of comments:
the answer is yes, you can use SQLite for this architecture.
You may also want to enable WAL (Write-Ahead Logging), in your case for increased concurrency:
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There are advantages and disadvantages to using WAL instead of a rollback journal. Advantages include:
WAL is significantly faster in most scenarios.
WAL provides more concurrency as readers do not block writers and a writer does not block readers. Reading and writing can proceed concurrently.
Disk I/O operations tends to be more sequential using WAL.
WAL uses many fewer fsync() operations and is thus less vulnerable to problems on systems where the fsync() system call is broken.
But there are also disadvantages:
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For the notification part of the question, I suggest to investigate triggers:
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Triggers are database operations that are automatically performed when a specified database event occurs.
Each trigger must specify that it will fire for one of the following operations: DELETE, INSERT, UPDATE.
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Related
In Stream Processing applications (f. e. based on Apache Flink or Apache Spark Streaming) it is sometimes necessary to process data exactly once.
In the database world something equal be achieved by using databases that follow the ACID criteria (correct me if I'm wrong here).
However there are a lot of (non relational) databases that do not follow ACID but BASE.
Now my question is: If I'm going to integrate such a BASE database into a stream processing application (exactly once), can I still guarantee exactly once processing for the whole pipeline? And if this is possible, under what circumstances?
Exactly Once Semantics means the processing framework such as flink can guarantee each incoming record(event) will be processed exactly one time even if the pineline fails in any way.
This is done by having checkpoints after each operation in the pineline, so that when the application recovers from failure, successful operation will not be executed again.
Depends on what kind of operations you are trying to do with databases, most cases databases are used as sinks for processing result to write into. In that case the operation involving database is just a simple insert and it will not be executed again after one successful run therefore it's still exactly-once regardless of its ACID support.
You might be tempted to group operations together for databases that support ACID but it will be a bad practice in a parallel streaming pineline since they created mutilple transactions and the locks might block the whole process. Instead, use BASE (NoSQL) database that are fast with intensive read and update performance is preferable, you just need to make your operations to be idempotent so that partially re-executed statements (if they failed half way through then after recovery they might be executed all again) won't result in incorrect data.
I have a big SQLite database to process, so I would like to use MPI for parallelization to accelerate the speed. What I want to do is sending a database from root to every slave, and sending the modified databases to root after slave add some table into it. I want to use MPI_Type_create_struct to create a datatype to store database, but the database is too complicated. IS there any other way to handle this situation? Thank you in advance!
I recently dealt with a similar problem - I have a large MPI application that uses SQLite as a configuration store. Handling multi-process writes is a challenge with an embedded SQL database. My experience with this involves a massively parallel application (running up to 65,535 ranks) with a shared filesystem.
Based on the FAQ from SQLite and some experience with database engines, there are a few ways to approach this problem. I am making the assumption that you are operating with a shared distributed file system, and multiple separate computers (a standard HPC cluster setup).
Since SQLite will block when multiple processes write to the database (but not read), reads will most likely not be an issue. Each process can run multiple SELECT commands at the same time without issue.
The challenge will be in the writing. Disk I/O is several orders of magnitude slower than computation, so generally this will be the bottleneck. Having said that, network communication may also be a significant slowdown, so how you approach the problem really depends on where the weakest link of your running environment will be.
If you have a fast network and slow disk speed, or if you want to implement this in the most straightforward way possible, your best bet is to have a single MPI rank in charge of writing to the database. Your compute processes would independently run SELECT commands until computation was complete, then send the new data to the MPI database process. The database control process would then write the new data to disk. I would not try to send the structure of the database across the network, rather I would send the data that should be written, along with (possibly) a flag that would identify what table/insert query the data should be written with. This technique is sort of similar to how a RDBMS works - while RDBMS servers do support concurrent writes, there is a "central" process in control of the ordering of write operations.
One thing to note is that if a process writes to the SQLite database, the file is locked for all processes that are trying to read or write to it. You will need to either handle the SQLITE_BUSY return code in your worker processes, register a callback to handle this, change the busy behavior, or use an alternate technique. In my application, I found that loading the database as an in-memory database, (https://www.sqlite.org/inmemorydb.html) for the readers provided a good workaround. Readers access the in-memory database, but sent results to the controlling process for writes. The downside is that you will have multiple copies of the database in memory.
Another option that might be less network intensive is to do the reads concurrently and have each worker process write out to their own file. You could write out to separate SQLite database files, or even export something like CSV (depending on the complexity of the data). When writes are complete, you would then have a single process merge the individual files into a single result database file - see How can I merge many SQLite databases?. This method has its own issues, but depending on where your bottlenecks are and how the system as a whole is laid out, this technique may work.
Finally, you might consider reading from the SQLite database and saving the data to a proper distributed file format, such as HDF5 (or using MPI IO). Once the computation is done, it would be pretty straightforward to write a script that would create a new SQLite database from this foreign file format.
I've noticed that Postgres now has built-in replication, including synchronous replication, streaming replication and some other variants. it even provides the ability to control synchrony for specific operations at the application-leve (e.g., use synchronous for important stuff like money transfers, but maybe don't for less critical things like user comments, etc.)
I'm working on a software using Django 1.5 (i.e, dev) and will possibly need synchronous replication (will have commerce related transactions going on).
Do you think that the built-in tools are best for the job in most cases, and do you have any thoughts on one variant of the built-in replication vs another, ease of use related, quality, etc.?
One final thing; Slony and PGPool II seem to be pretty popular (Slony, in particular) for replication. Is there A) a particular, technical reason for their popularity over built-in replication or B) is it just because a lot of people are using versions that don't have built-in replication, or C) have I been under a rock and PG built-in replication is already quite popular?
Update (more details)
I have only 2 physical servers, and they're located in the same rack. My intention is to provide a slave which can automatically turn into the master, should something go catastophically wrong in one machine (or even something simple like double power supply failure, etc.). I don't mind if my clients experience downtime during an automatic failover, so long as the downtime is a few minutes or so, not an hour or something.
I would like for zero data loss, and am willing to sacrifice more time in the failover process for that. Is there a way to make this trade off without going for synchronous replication (e.g, streaming logs without write back confirmation or something)?
What strategy or variant of replication would you recommend?
I think you misunderstand the benefits and cost of synchronous commits on replication. In PostgreSQL, replication works by recovering slaves up to the master, using the standard crash recovery features of PostgreSQL. In the event that, for example, the power goes out, you can be sure that the write-ahead log segments will be run on both master and slave. With asynchronous commit, the commit is written to the WAL, the application is notified and the slave is notified more or less all at the same time depending on network characteristics, etc.
Where synchronous commit comes in handy is where two things are true:
You have more than one slave (this is critical!) and
You need durability guarantees that asynchronous commits can't offer you.
With synchronous commit, the database waits until it hears back from a configurable number of slaves to tell the application that the commit has happened. This offers durability guarantees in a few cases where asynchronous commits are unable to work.
For example, suppose your master server takes a bullet through a raid array and immediately crashes (sorry, I couldn't think of any better examples with good hardware). Or suppose someone trips on a power cord and not only powers off the server but corrupts the software RAID device. In this case it is possible that a couple of transactions may not have been replicated and your WAL is unrecoverable, so those transactions are lost. With synchronized commit, the application would have waited until durability guarantees were met.
One thing this means is that if you do synchronous commit with only one slave your availability cannot outlast a crash on either master or slave, so your availability will be worse than it would have been with just one server. It also means that if your slave is geographically removed, that you introduce significant latency in your application's request to commit transactions.
Switching from async to sync commit it not a big change, but generally, I think that you get the most out of sync commit when you have already done as much as you can assurance and availability-wise on your hardware already. Start with async and move up when you can't further optimize your setup as async.
Re: "Slony and PGPool II seem to be pretty popular (Slony, in particular) for replication. Is there A) a particular, technical reason for their popularity over built-in replication or B) is it just because a lot of people are using versions that don't have built-in replication, or C) have I been under a rock and PG built-in replication is already quite popular?"
Slony is popular because it has been around for quite a long time, and the built-in PostgreSQL replication is relatively new. Cascading replication built in to PostgreSQL is even newer, and is something that Slony-I was built with.
There are two main advantages to Slony-I, first, you can replicate between differing versions of PostgreSQL, whereas the built-in replication system not only must use the same version, but the two servers must also be binary compatible. The other advantage is that you can replicate only certain tables on Slony-I instead of the whole database cluster. The disadvantages of Slony-I are numerous, and include poor user-friendliness, no synchronous commits, and difficult DDL (schema) changes. I believe that use of the built-in replication in Postgres will quickly exceed the Slony-I user base if it hasn't already done so.
As far as I remember, PGPool II uses statement-based replication (like what MySQL has had built-in), and is definitely the least desirable, in my opinion.
I would use the built-in hot standby/streaming replication in PostgreSQL. You can start with synchronous commit turned on and turn it off if you don't need it or the penalty is too high, or vice versa. Over a LAN, asynchronous mode seems to reach the slave in the order of a hundred milliseconds or so (from my own experience).
Some people tell me that we should avoid multiple apps reading/writing to the same database, because of concurrency issues. They were insistent to the point where they convinced me that a solid architecture only allows a single application to access the database. I don't know if such problems arise from READ operations concurrency, or if they are related only to WRITE operations.
My concern is that the DB is an excelent communication bus for some systems, like the one I'm developing. Are there any guidelines about how to do that while avoiding concurrency issues?
(1) Do not use a database as a communication bus unless you really have to. Given the open source message queuing systems available, I doubt that you do need to.
(2) If you have a transactional database, then concurrent read and write access should be fine, as long as your database has the necessary constraints and triggers to enforce data integritry (and all applications are appropriately tested to ensure in so far as possible that they are not inserting garbage).
(3) If you did not understand (1) or (2), you are not ready to use a database as a communications bus. Go read about databases, concurrency, and transactions.
What is the best IO strategy for a high traffic web app that logs user behaviour on a website and where ALL of the traffic will result in an IO write? Would it be to write to a file and overnight do batch inserts to the database? Or to simply do an INSERT (or INSERT DELAYED) per request? I understand that to consider this problem properly much more detail about the architecture would be needed, but a nudge in the right direction would be much appreciated.
By writing to the DB, you allow the RDBMS to decide when disk IO should happen - if you have enough RAM, for instance, it may be effectively caching all those inserts in memory, writing them to disk when there's a lighter load, or on some other scheduling mechanism.
Writing directly to the filesystem is going to be bandwidth-limited more-so than writing to a DB which then writes, expressly because the DB can - theoretically - write in more efficient sizes, contiguously, and at "convenient" times.
I've done this on a recent app. Inserts are generally pretty cheap (esp if you put them into an unindexed hopper table). I think that you have a couple of options.
As above, write data to a hopper table, if what ever application framework supports batched inserts, then use these, it will speed it up. Then every x requests, do a merge (via an SP call) into a master table, where you can normalize off data that has low entropy. For example if you are storing if the HTTP type of the request (get/post/etc), this can only ever be a couple of types, and better to store as an Int, and get improved I/O + query performance. Your master tables can also be indexed as you would normally do.
If this isn't good enough, then you can stream the requests to files on the local file system, and then have an out of band (i.e seperate process from the webserver) suck these files up and BCP them into the database. This will be at the expense of more moving parts, and potentially, a greater delay between receiving requests and them finding their way into the database
Hope this helps, Ace
When working with an RDBMS the most important thing is optimizing write operations to disk. Something somewhere has got to flush() to persistant storage (disk drives) to complete each transaction which is VERY expensive and time consuming. Minimizing the number of transactions and maximizing the number of sequential pages written is key to performance.
If you are doing inserts sending them in bulk within a single transaction will lead to more effecient write behavior on disk reducing the number of flush operations.
My recommendation is to queue the messages and periodically .. say every 15 seconds or so start a transaction ... send all queued inserts ... commit the transaction.
If your database supports sending multiple log entries in a single request/command doing so can have a noticable effect on performance when there is some network latency between the application and RDBMS by reducing the number of round trips.
Some systems support bulk operations (BCP) providing a very effecient method for bulk loading data which can be faster than the use of "insert" queries.
Sparing use of indexes and selection of sequential primary keys help.
Making sure multiple instances either coordinate write operations or write to separate tables can improve throughput in some instances by reducing concurrency management overhead in the database.
Write to a file and then load later. It's safer to be coupled to a filesystem than to a database. And the database is more likely to fail than the your filesystem.
The only problem with using the filesystem to back writes is how you extend the log.
A poorly implemented logger will have to open the entire file to append a line to the end of it. I witnessed one such example case where the person logged to a file in reverse order, being the most recent entries came out first, which required loading the entire file into memory, writing 1 line out to the new file, and then writing the original file contents after it.
This log eventually exceeded phps memory limit, and as such, bottlenecked the entire project.
If you do it properly however, the filesystem reads/writes will go directly into the system cache, and will only be flushed to disk every 10 or more seconds, ( depending on FS/OS settings ) which has a negligible performance hit compared to writing to arbitrary memory addresses.
Oh yes, and whatever system you use, you'll need to think about concurrent log appending. If you use a database, a high insert load can cause you to have deadlock conditions, and on files, you need to make sure that you're not going to have 2 concurrent writes cancel each other out.
The insertions will generally impact the (read/update) performance of the table. Perhaps you can do the writes to another table (or database) and have batch job that processes this data. The advantages of the database approach is that you can query/report on the data and all the data is logically in a relational database and may be easier to work with. Depending on how the data is logged to text file, you could open up more possibilities for corruption.
My instinct would be to only use the database, avoiding direct filesystem IO at all costs. If you need to produce some filesystem artifact, then I'd use a nightly cron job (or something like it) to read DB records and write to the filesystem.
ALSO: Only use "INSERT DELAYED" in cases where you don't mind losing a few records in the event of a server crash or restart, because some records almost certainly WILL be lost.
There's an easier way to answer this. Profile the performance of the two solutions.
Create one page that performs the DB insert, another that writes to a file, and another that does neither. Otherwise, the pages should be identical. Hit each page with a load tester (JMeter for example) and see what the performance impact is.
If you don't like the performance numbers, you can easily tweak each page to try and optimize performance a bit or try new solutions... everything from using MSMQ backed by MSSQL to delayed inserts to shared logs to individual files with a DB background worker.
That will give you a solid basis to make this decision rather than depending on speculation from others. It may turn out that none of the proposed solutions are viable or that all of them are viable...
Hello from left field, but no one asked (and you didn't specify) how important is it that you never, ever lose data?
If speed is the problem, leave it all in memory, and dump to the database in batches.
Do you log more than what would be available in the webserver logs? It can be quite a lot, see Apache 2.0 log information for example.
If not, then you can use the good old technique of buffering then batch writing. You can buffer at different places: in memory on your server, then batch insert them in db or batch write them in a file every X requests, and/or every X seconds.
If you use MySQL there are several different options/techniques to load efficiently a lot of data: LOAD DATA INFILE, INSERT DELAYED and so on.
Lots of details on insertion speeds.
Some other tips include:
splitting data into different tables per period of time (ie: per day or per week)
using multiple db connections
using multiple db servers
have good hardware (SSD/multicore)
Depending on the scale and resources available, it is possible to go different ways. So if you give more details, i can give more specific advices.
If you do not need to wait for a response such as a generated ID, you may want to adopt an asynchronous strategy using either a message queue or a thread manager.