Thus a COMMIT can happen without ever writing to the original database, which allows readers to continue operating from the original unaltered database while changes are simultaneously being committed into the WAL.
WAL provides more concurrency as readers do not block writers and a writer does not block readers. However, the developers do not think this is a major concern since the wal-index rarely exceeds 32 KiB in size and is never synced. The only way we have found to guarantee that all processes accessing the same database file use the same shared memory is to create the shared memory by mmapping a file in the same directory as the database itself.
But for any particular reader, the end mark is unchanged for the duration of the transaction, thus ensuring that a single read transaction only sees the database content as it existed at a single point in time. A checkpoint operation takes content from the WAL file and transfers it back into the original database file.
The checkpoint remembers in the wal-index how far it got and will resume transferring content from the WAL to the database from where it left off on the next invocation.
Thus, if an older version of SQLite attempts to connect to an SQLite database that is operating in WAL mode, it will report an error along the lines of "file is encrypted or is not a database".
A checkpoint is only able to run to completion, and reset the WAL file, if there are no other database connections using the WAL file. The checkpoint will do as much work as it can without upsetting the reader, but it cannot run to completion. This repeats until some checkpoint is able to complete.
Because the WAL can be growing and adding new commit records while various readers connect to the database, each reader can potentially have its own end mark. There are Pros and Cons for both approaches. I expected this configuration to be the best by far, but it turned out to be not much better than the previous test.
The following bullets enumerate some of the ways that this can happen and how to avoid them. Therefore, it is also less susceptible to issues that arise on system where system call for fsync is broken.
However, with older versions of SQLite, the same page might be written into the WAL file multiple times if the transaction grows larger than the page cache. SQLite Optimization Experiments I wrote a simple test script that creates a database, and then performs cycles while recording the time.
Multiple transactions can be appended to the end of a single WAL file. Checkpointing Of course, one wants to eventually transfer all the transactions that are appended in the WAL file back into the original database.
Also, if multiple processes are to access a WAL mode database, then all processes should run under user or group IDs that give them write access to the database files, the WAL file, the shared memory -shm file, and the containing directory. The only way we have found to guarantee that all processes accessing the same database file use the same shared memory is to create the shared memory by mmapping a file in the same directory as the database itself.
I expected this run to be much like the first optimized run but without the spike of 0. Whenever a write operation occurs, the writer checks how much progress the checkpointer has made, and if the entire WAL has been transferred into the database and synced and if no readers are making use of the WAL, then the writer will rewind the WAL back to the beginning and start putting new transactions at the beginning of the WAL.
Thus a long-running read transaction can prevent a checkpointer from making progress. Write-Ahead Log acts more instantly in most of the conditions.
An exclusive lock is held during recovery. One quick look at the SQLite documentation and you will see that there are quite a lot of configuration settings that can be changed to tweak performance to your needs.Write-Ahead Logging & Single Transaction.
The third test was a combination of the first two. I set the journaling mode to WAL and grouped the operations in a single transaction.
I expected this run to be much like the first optimized run but without the spike of. Beginning with version (), a new "Write-Ahead Log" option (hereafter referred to as "WAL") is available. There are advantages and disadvantages to using WAL instead of a rollback journal.
@sixfeetsix: The problem is "pragma journal_mode=WAL" is supported by SQLite but there are no compatible jdbc drivers for this new version and the default.
Everyone loves SQLite. It is everywhere, it is fast and it is easy to use. Want to know how to make it even faster? Here’s the secret: enable Write-Ahead Logging (aka WAL). Normally when you update your SQLite Database within a transaction, the original data is copied to a rollback file.
The new data is written directly to the DB file. SQLite and Write Ahead Logging: why so large files. the auxiliary write ahead log and shared memory files used for transaction control are automatically deleted when the latest connection to the database closes.
(If you have disabled deleting WAL files with SQLITE_FCNTL_PERSIST_WAL, you have to set PRAGMA journal_size_limit to a. A WAL (write-ahead log) mode is introduced as of SQLite This mode is persistent, and it stays in effect even after closing and reopening the database.
This mode is persistent, and it stays in effect even after closing and reopening the database.Download