Does SQLite3 safely handle concurrent access by multiple processes reading/writing from the same DB?开发者_运维问答 Are there any platform exceptions to that?

---

If most of those concurrent accesses are reads (e.g. SELECT), SQLite can handle them very well. But if you start writing concurrently, lock contention could become an issue. A lot would then depend on how fast your filesystem is, since the SQLite engine itself is extremely fast and has many clever optimizations to minimize contention. Especially SQLite 3.

For most desktop/laptop/tablet/phone applications, SQLite is fast enough as there's not enough concurrency. (Firefox uses SQLite extensively for bookmarks, history, etc.)

For server applications, somebody some time ago said that anything less than 100K page views a day could be handled perfectly by a SQLite database in typical scenarios (e.g. blogs, forums), and I have yet to see any evidence to the contrary. In fact, with modern disks and processors, 95% of web sites and web services would work just fine with SQLite.

If you want really fast read/write access, use an in-memory SQLite database. RAM is several orders of magnitude faster than disk.

---

Yes it does. Lets figure out why

SQLite is transactional

All changes within a single transaction in SQLite either occur completely or not at all

Such ACID support as well as concurrent read/writes are provided in 2 ways - using the so-called journaling (lets call it “old way”) or write-ahead logging (lets call it “new way”)

Journaling (Old Way)

In this mode SQLite uses DATABASE-LEVEL locking. This is the crucial point to understand.

That means whenever it needs to read/write something it first acquires a lock on the ENTIRE database file. Multiple readers can co-exist and read something in parallel

During writing it makes sure an exclusive lock is acquired and no other process is reading/writing simultaneously and hence writes are safe.

This is why here they’re saying SQlite implements serializable transactions

Troubles

As it needs to lock an entire database every time and everybody waits for a process handling writing concurrency suffers and such concurrent writes/reads are of fairly low performance

Rollbacks/outages

Prior to writing something to the database file SQLite would first save the chunk to be changed in a temporary file. If something crashes in the middle of writing into the database file it would pick up this temporary file and revert the changes from it

Write-Ahead Logging or WAL (New Way)

In this case all writes are appended to a temporary file (write-ahead log) and this file is periodically merged with the original database. When SQLite is searching for something it would first check this temporary file and if nothing is found proceed with the main database file.

As a result, readers don’t compete with writers and performance is much better compared to the Old Way.

Caveats

SQlite heavily depends on the underlying filesystem locking functionality so it should be used with caution, more details here

You're also likely to run into the database is locked error, especially in the journaled mode so your app needs to be designed with this error in mind

---

Yes, SQLite handles concurrency well, but it isn't the best from a performance angle. From what I can tell, there are no exceptions to that. The details are on SQLite's site: https://www.sqlite.org/lockingv3.html

This statement is of interest: "The pager module makes sure changes happen all at once, that either all changes occur or none of them do, that two or more processes do not try to access the database in incompatible ways at the same time"

---

Nobody seems to have mentioned WAL (Write Ahead Log) mode. Make sure the transactions are properly organised and with WAL mode set on, there is no need to keep the database locked whilst people are reading things whilst an update is going on.

The only issue is that at some point the WAL needs to be re-incorporated into the main database, and it does this when the last connection to the database closes. With a very busy site you might find it take a few seconds for all connections to be close, but 100K hits per day should not be a problem.

---

In 2019, there are two new concurrent write options not released yet but available in separate branches.

"PRAGMA journal_mode = wal2"

The advantage of this journal mode over regular "wal" mode is that writers may continue writing to one wal file while the other is checkpointed.

BEGIN CONCURRENT - link to detailed doc

The BEGIN CONCURRENT enhancement allows multiple writers to process write transactions simultanously if the database is in "wal" or "wal2" mode, although the system still serializes COMMIT commands.

When a write-transaction is opened with "BEGIN CONCURRENT", actually locking the database is deferred until a COMMIT is executed. This means that any number of transactions started with BEGIN CONCURRENT may proceed concurrently. The system uses optimistic page-level-locking to prevent conflicting concurrent transactions from being committed.

Together they are present in begin-concurrent-wal2 or each in a separate own branch.

---

SQLite has a readers-writer lock on the database level. Multiple connections (possibly owned by different processes) can read data from the same database at the same time, but only one can write to the database.

SQLite supports an unlimited number of simultaneous readers, but it will only allow one writer at any instant in time. For many situations, this is not a problem. Writer queue up. Each application does its database work quickly and moves on, and no lock lasts for more than a few dozen milliseconds. But there are some applications that require more concurrency, and those applications may need to seek a different solution. -- Appropriate Uses For SQLite @ SQLite.org

The readers-writer lock enables independent transaction processing and it is implemented using exclusive and shared locks on the database level.

An exclusive lock must be obtained before a connection performs a write operation on a database. After the exclusive lock is obtained, both read and write operations from other connections are blocked till the lock is released again.

Implementation details for the case of concurrent writes

SQLite has a lock table that helps locking the database as late as possible during a write operation to ensure maximum concurrency.

The initial state is UNLOCKED, and in this state, the connection has not accessed the database yet. When a process is connected to a database and even a transaction has been started with BEGIN, the connection is still in the UNLOCKED state.

After the UNLOCKED state, the next state is the SHARED state. In order to be able to read (not write) data from the database, the connection must first enter the SHARED state, by getting a SHARED lock. Multiple connections can obtain and maintain SHARED locks at the same time, so multiple connections can read data from the same database at the same time. But as long as even only one SHARED lock remains unreleased, no connection can successfully complete a write to the database.

If a connection wants to write to the database, it must first get a RESERVED lock.

Only a single RESERVED lock may be active at one time, though multiple SHARED locks can coexist with a single RESERVED lock. RESERVED differs from PENDING in that new SHARED locks can be acquired while there is a RESERVED lock. -- File Locking And Concurrency In SQLite Version 3 @ SQLite.org

Once a connection obtains a RESERVED lock, it can start processing database modification operations, though these modifications can only be done in the buffer, rather than actually written to disk. The modifications made to the readout content are saved in the memory buffer. When a connection wants to submit a modification (or transaction), it is necessary to upgrade the RESERVED lock to an EXCLUSIVE lock. In order to get the lock, you must first lift the lock to a PENDING lock.

A PENDING lock means that the process holding the lock wants to write to the database as soon as possible and is just waiting on all current SHARED locks to clear so that it can get an EXCLUSIVE lock. No new SHARED locks are permitted against the database if a PENDING lock is active, though existing SHARED locks are allowed to continue.

An EXCLUSIVE lock is needed in order to write to the database file. Only one EXCLUSIVE lock is allowed on the file and no other locks of any kind are allowed to coexist with an EXCLUSIVE lock. In order to maximize concurrency, SQLite works to minimize the amount of time that EXCLUSIVE locks are held. -- File Locking And Concurrency In SQLite Version 3 @ SQLite.org

So you might say SQLite safely handles concurrent access by multiple processes writing to the same DB simply because it doesn't support it! You will get SQLITE_BUSY or SQLITE_LOCKED for the second writer when it hits the retry limitation.

---

This thread is old but i think it would be good to share result of my tests done on sqlite: i ran 2 instances of python program (different processes same program) executing statements SELECT and UPDATE sql commands within transaction with EXCLUSIVE lock and timeout set to 10 seconds to get a lock, and result were frustrating. Every instance did in 10000 step loop:

• connect to db with exclusive lock
• select on one row to read counter
• update the row with new value equal to counter incremented by 1
• close connection to db

Even if sqlite granted exclusive lock on transaction, the total number of really executed cycles were not equal to 20 000 but less (total number of iterations over single counter counted for both processes). Python program almost did not throw any single exception (only once during select for 20 executions). sqlite revision at moment of test was 3.6.20 and python v3.3 CentOS 6.5. In mine opinion it is better to find more reliable product for this kind of job or restrict writes to sqlite to single unique process/thread.

---

It is natural when you specify the name for db or even in memory db if you have concurrent access (specially write) you will get this.
In my case, I am using Sqlite for testing and it is because there are several tests in the same solution it happens.
You can have two improvements:

1. Delete before creating db.Database.EnsureDeletedAsync();
2. Use an empty string for connection, in this case it will create a random name each call:

{
  "ConnectionStrings": {
    "ConnectionType": "sqlite",
    "ConnectionString": ""
  }
}