So I know that autocommit commits every sql statement, but do updates to the database go directly to the disk or do they remain on cache until flushed?
I realize it's dependent on the database implementation.
Does auto-commit mean
a) every statement is a complete transaction AND it goes straight to disk or
b) every statement is a complete transaction and it may go to cache where it will be flushed later or it may go straight to disk
Clarification would be great.
Auto-commit simply means that each statement is in its own transaction which commits immediately. This is in contrast to the "normal" mode, where you must explicitly BEGIN a transaction and then COMMIT once you are done (usually after several statements).
The phrase "auto-commit" has nothing to do with disk access or caching. As an implementation detail, most databases will write to disk on commit so as to avoid data loss, but this isn't mandatory in the spec.
For ARIES-based protocols, committing a transaction involves logging all modifications made within that transaction. Changes are flushed immediately to logfile, but not necessarily to datafile (that is dependent on the implementation). That is enough to ensure that the changes can be recovered in the event of a failure. So, (b).
Commit provides no guarantee that something has been written to disk, only that your transaction has been completed and the changes are now visible to other users.
Permanent does not necessarily mean written to disk (i.e. durable)... Even if a "commit" waits for the transaction to complete can be configured with some databases.
For example, Oracle 10gR2 has several commit modes, including IMMEDIATE,WAIT,BATCH,NOWAIT. BATCH will queue the buffer the changes and the writer will write the changes to disk at some future time. NOWAIT will return immediately without regard for I/O.
The exact behavior of commmit is very database specific and can often be configured depending on your tolerance for data loss.
It depends on the DBMS you're using. For example, Firebird has it as an option in configuration file. If you turn Forced Writes on, the changes go directly to the disk. Otherwise they are submitted to the filesystem, and the actual write time depends on the operating system caching.
If the database transaction is claimed to be ACID, then the D (durability) mandates that the transaction committed should survive the crash immediately after the successful commit. For single server database, that means it's on the disk (disk commit). For some modern multi-server databases, it can also means that the transaction is sent to one or more servers (network commit, which are typically much faster than disk), under the assumption that the probability of multiple server crash at the same time is much smaller.
It's impossible to guarantee that commits are atomic, so modern databases use two-phase or three phase commit strategies. See Atomic Commit
Related
We have a huge DML script, that opens up a transaction and performs a lot of changes and only then it commits.
So recently, I had triggered this scripts (through an app), and as it was taking quite an amount of time, I had killed the session, which triggered a ROLLBACK.
So the problem is that this ROLLBACK took forever and moreover it was hogging a lot of CPU (100% utilization), and as I was monitoring this session (using exec DMVs), I saw a lot of waits that are IO related (IO_COMPLETION, PAGE_IO_LATCH etc).
So my question is:
1. WHy does a rollback take some much amount of time? Is it because it needs to write every revert change to the LOG file? And the IO waits I saw could be related to IO operation against this LOG file?
2. Are there any online resources that I can find, that explains how ROLLBACK mechanism works?
Thank You
Based on another article on the DBA side of SO, ROLLBACKs are slower for at least two reasons: the original SQL is capable of being multithreaded, where the rollback is single-threaded, and two, a commit confirms work that is already complete, where the rollback not only must identify the log action to reverse, but then target the impacted row.
https://dba.stackexchange.com/questions/5233/is-rollback-a-fast-operation
This is what I have found out about why a ROLLBACK operation in SQL Server could be time-consuming and as to why it could produce a lot of IO.
Background Knowledge (Open Tran/Log mechanism):
When a lot of changes to the DB are being written as part of an open transaction, these changes modify the data pages in memory (dirty pages) and log records (into a structure called LOG BLOCKS) generated are initially written to the buffer pool (In Memory). These dirty pages are flushed to the disk either by a recurring Checkpoint operation or a lazy-write process. In accordance with the write-ahead logging mechanism of the SQL Server, before the dirty pages are flushed the LOG RECORDS describing these changes needs to be flushed to the disk as well.
Keeping this background knowledge in mind, now when a transaction is rolled back, this is almost like a recovery operation, where all the changes that are written to the disk, have to be undone. So, the heavy IO we were experiencing might have happened because of this, as there were lots of data changes that had to be undone.
Information Source: https://app.pluralsight.com/library/courses/sqlserver-logging/table-of-contents
This course has a very deep and detailed explanation of how logging recovery works in SQL Server.
I have a lot of small transactions. I perform them in some sequence one by one with XA JDBC driver in Oracle RAC. Is there guarantee they are committed in the same order I call them?
Update:
All is happening in a single database session.
Yes, they are.
In a RAC or non-RAC environment, when you get a successful execution of a commit, it means that the database change has been guaranteed recorded on disk (in the REDO log). When the log writer writes to the redo log, it increments the SCN (System Change Number) which is global across all instances in the RAC. You can see the database's current SCN by querying select current_scn from v$database; if you have sufficient privileges.
There are some non-default options for commit where it doesn't necessarily wait until the redo log write is complete before it returns a success. If you use them, then possibly multiple transactions can be batched within the same SCN. That would permit a commit on a different instance to grab an SCN prior to one being issued for the batch. Avoid those options, and you'll be fine.
We are performing quite large transactions on a SQLite database that is causing the WAL file to grow extremely large. (Sometimes up to 1GB for large transactions.) Is there a way to checkpoint the WAL file while in the middle of a transaction? When I try calling sqlite3_wal_checkpoint() or executing the WAL checkpoint PRAGMA statement, both return SQLITE_BUSY.
Not really. This is whole point of transactions: WAL (or journal file) keeps data that would become official once successfully committed. Until that happens, if anything goes wrong - program crash, computer reboot, etc, WAL or journal file allow to safely rollback (undo) uncommitted action. Moving only part of this uncommitted transaction would defeat the purpose.
Note that SQLite documentations defines check-pointing as moving the WAL file transactions back into the database. In other words, checkpointing moves one or more transactions from WAL, but not part of huge uncommitted transaction.
There are few possible solutions to your problem:
Avoid huge transactions - commit in smaller chunks if you can. Of course, this is not always possible, depending on your application.
Use old journaling mode with PRAGMA journal_mode=DELETE. It is slightly slower than new WAL mode (with PRAGMA journal_mode=WAL), but in my experience it tends to create much smaller journal files, and they get deleted when transaction successfully commits. For example, Android 4.x is still using old journaling mode - it tends to work faster on flash and does not create huge temporary or journal files.
I'm looking into optimizing throughput in a Java application that frequently (100+ transactions/second) updates data in a Postgresql database. Since I don't mind losing a few transactions if the database crashes, I think that using asynchronous commit could be a good fit.
My only concern is that I don't want a delay after commit until other transactions/queries see my commit. I am using the default isolation level "Read committed".
So my question is: Does using asynchronous commit in Postgresql in any way mean that there will be delays before other transactions see my committed data or before they proceed if they have been waiting for my transaction to complete? (As mentioned before, I don't care if there is a delay before my data is persisted to disk.)
It would seem that this is the behavior you're looking for.
http://www.postgresql.org/docs/current/static/wal-async-commit.html
Selecting asynchronous commit mode means that the server returns success as soon as the transaction is logically completed, before the
WAL records it generated have actually made their way to disk. This
can provide a significant boost in throughput for small transactions.
The WAL is used to provide on-disk data integrity, and has nothing to do about table integrity of a running server; it's only important if the server crashes. Since they specifically mention "as soon as the transaction is logically completed", the documention is indicating that this does not affect table behavior.
I have n machines writing to DB (sql server) at the exact same time (initiating a transaction). I'm setting the Isolation level to serializable. My understanding is that whichever machine's transaction gets to the DB first, gets executed and the other transactions will be blocked while this completes.
Is that correct?
It depends - are they all performing the same activities? That is, exactly the same statements executing in the same order, with no flow control?
If not, and two connections are accessing independent objects in the DB, they can run in parallel.
If there's some overlap of resources, then some progress may be made by multiple connections until one of them wants to take a lock that another already has - at which point it will wait. There is then the possibility of deadlocks.
SERIALIZABLE:
Statements cannot read data that has been modified but not yet committed by other transactions.
No other transactions can modify data that has been read by the current transaction until the current transaction completes.
Other transactions cannot insert new rows with key values that would fall in the range of keys read by any statements in the current transaction until the current transaction completes.
whichever machine's transaction gets to the DB first, gets executed and the other transactions will be blocked while this completes
No, this i incorrect. The results should be as if each transaction was executed one after another (serially, hence the isolation level name). But the engine is free to use any implementation it likes, as long as it honors the guarantees of the serializable isolation model. And some engines actually implement it pretty much as you describe it, eg. Redis Transactions (although Redis has no 'isolation level' concept).
For SQL Server the transactions will execute in parallel until they hit a lock conflict. When a conflict occurs the transaction that has the lock granted continues undisturbed, while the one that requests the lock in a conflicting mode has to wait for the lock to free (for the granted transaction to commit). Which transaction happens to be the request and which one happens to be the granted is entirely up to what is being executed. That means that it well may be the case that the second machine gets the grant first and finishes first, while the first machine waits.
For a better understanding how the locking behavior differs under serializable isolation level, see Key-Range Locking
Yes, this will be true for write operations at any isolation level: "My understanding is that whichever machine's transaction gets to the DB first, gets executed and the other transactions will be blocked while this completes."
The isolation level helps determine what happens when you READ data while this is going on. Serializable read operations will block your write operations, which might be the behavior you want.