![]() Like a filesystem, it has free pages and pages in-use. What does this buy you? It helps to think of the SQLite database as a kind of dynamically resizable filesystem-in-miniature. Note that SQLite is doing this in a transaction-safe way, that is, if the power fails at any step, you’re not hosed. There’s more to the process than this, but I suspect conceptually that’s the bulk of the operation. Finally, it renames the new database to the original’s file name.SQLite deletes the original database file.(Note that rowids are not preserved unless they were marked as INTEGER PRIMARY KEY.) For each table in the original, SQLite reads each row sequentially and writes it to the new database.This creates the tables, indices, and so forth. It reads the original database’s entire schema and applies it to the new one.SQLite creates an empty database file in the same directory as the original database file.Here’s my guess as to the simplified steps SQLite actually performs when instructed to VACUUM a database: To step back, what exactly does VACUUM do? According to SQLite’s documentation, VACUUM “rebuilds the entire database.” Okay…so what does that do? But the solution comes at a price, and a pretty steep one. VACUUM sounds like a magic pill, this one-shot solution that will solve all your problems. ![]() If you look around the Internet, you’ll find lots of people asking how to achieve one or both with SQLite. More on that later.)Ĭonserving disk space and improving performance are really tantilizing things. (There’s a third, more esoteric reason: certain configuration changes require a VACUUM. Why would you want to VACUUM an SQLite database? To conserve disk space and improve performance. In this case, by deleting a significant amount of data from the database I now confronted a question all applications using SQLite face at some point: to VACUUM or not to VACUUM? What is VACUUM? As you might imagine, I was surprised to see the file size had not changed-but only for a moment.Īs is true so often in programming, closing one door tends to open two or more new doors. (Yeah, fixing this bug was long overdue.) After it deleted them I quickly checked the size of the file to see what kind of disk savings I’d just earned for myself. When I first ran the new garbage collector on my four year-old 2.7GB SQLite database, it detected a tremendous number of emails to remove-almost 50,000 messages. (If you’re interested in the problem and why Geary didn’t simply delete an email when it was not visible in a mail folder, the original ticket is a good place to start.) It was a classic problem of garbage collection and reference counting. The long and short of it is, prior to this commit, Geary never deleted emails from its internal SQLite database, even if they were deleted on the server. For storing around 100 million rows of around 90 byes each, what do you expect the best case insertion rate to be? I'm trying to set my own expectations of what we can achieve with our current approach.The week before Christmas I hunkered down and took on a nagging issue in Geary I’m almost embarrassed to discuss.Why does memcpy() hog so much of the CPU when we're only storing around ~1.5MB/sec? Is this normal?.97% of the time is spent in sqlite3BtreeMovetoUnpacked(). I see that around 75% of the time is spent in memcpy_repmovs(). We compile sqlite with SQLITE_WIN32_MALLOC and SQLITE_WIN32_HEAP_CREATE. My machine is on Azure with 12 cores and has 128 GB of RAM. Currently it takes around 90 minutes which means the insertion rate is ~18000 rows/second. The DB grows to ~9GB after it's completely populated. We need to create the DB from scratch on launch and populate 100 million rows in the table. ![]() We have an in-memory database with a simple table(3 integers and a string, the first 3 numbers form the primary key, no other indexes at this point ). ![]()
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