What could cause a sqlite application to slow down over time with high load? - sqlite

I'll definitely need to update this based on feedback so I apologize in advance.
The problem I'm trying to solve is roughly this.
The graph shows Disk utilization in the Windows task manager. My sqlite application is a webserver that takes in json requests with timestamps, looks up the existing entry in a 2 column key/value table, merges the request into the existing item (they don't grow over time), and then writes it back to the database.
The db is created as follows. I've experimented with and without WAL without difference.
createStatement().use { it.executeUpdate("CREATE TABLE IF NOT EXISTS items ( key TEXT NOT NULL PRIMARY KEY, value BLOB );") }
The write/set is done as follows
try {
val insertStatement = "INSERT OR REPLACE INTO items (key, value) VALUES (?, ?)"
prepareStatement(insertStatement).use {
it.setBytes(1, keySerializer.serialize(key))
it.setBytes(2, valueSerializer.serialize(value))
it.executeUpdate()
}
commit()
} catch (t: Throwable) {
rollback()
throw t
}
I use a single database connection the entire time which seems to be ok for my use case and greatly improves performance relative to getting a new one for each operation.
val databaseUrl = "jdbc:sqlite:${System.getProperty("java.io.tmpdir")}/$name-map-v2.sqlite"
if (connection?.isClosed == true || connection == null) {
connection = DriverManager.getConnection(databaseUrl)
}
I'm effectively serializing access to the db. I'm pretty sure the default threading mode for the sqlite driver is to serialize and I'm also doing some serializing in kotlin coroutines (via actors).
I'm load testing the application locally and I notice that disk utilization spikes around the one minute mark but I can't determine why. I know that throughput plummets when that happens though. I expect the server to chug along at a more or less constant rate. The db in these tests is pretty small too, hardly reaches 1mb.
Hoping people can recommend some next steps or set me straight as far as performance expectations. I'm assuming there is some sqlite specific thing that happens when throughput is very high for too long, but I would have thought it would be related to WAL or something (which I'm not using).

I have a theory but it's a bit farfetched.
The fact that you hit a performance wall after some time makes me think that either a buffer somewhere is filling up, or some other kind of data accumulation threshold is being reached.
Where exactly the culprit is, I'm not sure.
So, I'd run the following tests.
// At the beginning
connection.setAutoCommit(true);
If the problem is in the driver side of the rollback transaction buffer, then this will slightly (hopefully) slow down operations, "spreading" the impact away from the one-minute mark. Instead of getting fast operations for 59 seconds and then some seconds of full stop, you get not so fast operations the whole time.
In case the problem is further down the line, try
PRAGMA JOURNAL_MODE=MEMORY
PRAGMA SYNCHRONOUS=OFF disables the rollback journal synchronization
(The data will be more at risk in case of a catastrophic powerdown).
Finally, another possibility is that the page translation buffer gets filled after a sufficient number of different keys has been entered. You can test this directly by doing these two tests:
1) pre-fill the database with all the keys in ascending order and a large request, then start updating the same many keys.
2) run the test with only very few keys.
If the slowdown does not occur in the above cases, then it's either TLB buffer management that's not up to the challenge, or database fragmentation is a problem.
It might be the case that issuing
PRAGMA PAGE_SIZE=32768
upon database creation might solve or mitigate the problem. Conversely, PRAGMA PAGE_SIZE=1024 could "spread" the problem avoiding performance bottlenecks.
Another thing to try is closing the database connection and reopening it when it gets older than, say, 30 seconds. If this works, we'll still need to understand why it works (in this case I expect the JDBC driver to be at fault).

First of all, I want to say that I do not use exactly your driver for sqlite, and I use different devices in my work. (but how different are they really?)
From what I see, correct me if im wrong, you use one transaction, for one insert statement. You get request, you use the disc, you use the memory, open, close etc... every time. This can't work fast.
The first thing I do when I have to do inserts in sqlite is to group them, and use a single transaction to do it. That way, you are using your resources in batches.
One transaction, many insert statements, single commit. If there is a problem with a batch, handle the valid separately, log the faulty, move the next batch of requests.

Related

AWS DAX Performance issues with table scan

Hi I am working on an project that requires to bring all dyanamo db document in memory. I will be using table.scan() boto3 method which nearly takes 33 seconds for all 10k records.
I have configured the DAX and using it for table scan, which takes nearly the 42 seconds with same 10k records with same lambda configuration. I tried multiple times results are same.
I tried below code :
daxclient = amazondax.AmazonDaxClient.resource(endpoint_url="...")
table = daxclient.Table('table_name')
start_time = time.perf_counter()
retry = True
while retry:
try:
response = table.scan(TableName ="table_name")
retry = 'LastEvaluatedKey' in response
scan_args['ExclusiveStartKey'] = response.get('LastEvaluatedKey')
except Exception as e:
print(e)
print(time.perf_counter()-start_time)
I tried boto3 getItem() method this becomes faster like first time it takes 0.4seconds and after that it takes 0.01 seconds.
Not sure why it is not working with table scan method.
Please suggest.
DAX doesn’t cache scan results. You therefore shouldn’t expect a performance boost and, since you’re bouncing through an extra server on the way to the database, can expect a performance penalty.
You must have very large items to see these performance numbers. And you’re doing a scan a lot? You might want to double check DynamoDB is the right fit.

Is there an efficient way to process data expiration / massive deletion (to free space) with Riak on leveldb?

On Riak :
Is there a way to process data expiration or to dump old data to free some space?
Is it efficient ?
Edit: Thanks to Joe to provide the answer and its workaround (answer down).
Data expiration should be thought from the very beginning as it requires an additional index with a map-reduce algorithme.
Short answer: No, there is no publisher-provided expiry.
Longer answer: Include the write time, in a integer representation like Unix epoch, in a secondary index entry on each value that you want to be subject to expiry. The run a periodic job during off-peak times to do a ranged 2I query to get any entries from 0 to (now - TTL). This could be used as an input to a map/reduce job to do the actual deletes.
As to recovering disk space, leveldb is very slow about that. When a value is written to leveldb it starts in level 0, then as each level fills, compaction moves values to the next level, so your least recently written data resides on disk in the lowest levels. When you delete a value, a tombstone is written to level 0, which masks the previous value in the lower level, and as normal compaction occurs the tombstone is moved down as any other value would be. The disk space consumed by the old value is not reclaimed until the tombstone reaches the same level.
I have written a little c++ tool that uses the leveldb internal function CompactRange to perform this task. Here you can read the article about this.
With this we are able to delete an entire bucket (key by key) and wipe all tombstones. 50Gb of 75Gb are freed!
Unfortunately, this only works if leveldb is used as backend.

SQLITE database WAL file size keeps growing

I am writing continuously into a db file which has PRAGMA journal_mode=WAL, PRAGMA journal_size_limit=0. My C++ program has two threads, one reader(queries at 15 sec intervals) and one writer(inserts at 5 sec intervals).
Every 3 min I am pausing insertion to run a sqlite3_wal_checkpoint_v2() from the writer thread with the mode parameter as SQLITE_CHECKPOINT_RESTART. To ensure that no active read operations are going on at this point, I set a flag that checkpointing is about to take place and wait for reader to complete (the connection is still open) before running checkpoint. After checkpoint completion I again indicate to readers it is okay to resume querying.
sqlite3_wal_checkpoint_v2() returns SQLITE_OK, and pnLog and Ckpt as equal(around 4000), indicating complete wal file has been synced with main db file. So next write should start from beginning according to documentation. However, this does not seem to be happening as the subsequent writes cause the WAL file to grow indefinitely, eventually up to some GBs.
I did some searching and found that that readers can cause checkpoint failure due to open transactions. However, the only reader I'm using is ending its transaction before the checkpoint starts. What else could be preventing the WAL file from not growing?
This is far too late as an answer, but may be useful to other people.
According to the SQLite documentation, your expectations should be correct, but if you read this SO post, problems arise also in case of non-finalized statements. Therefore, if you just sqlite3_reset() your statement, there are chances anyway that the db may look busy or locked for a checkpoint. Note that this may happen also with higher levels of SQLITE_CHECKPOINT_values.
Also, the SQLITE_CHECKPOINT_TRUNCATE value, if checkout is successfully operated, will truncate the -wal file to zero length. That may help you check that all pages have been inserted in the db.
Another discussion in which -wal files grow larger and larger due to unfinalized statements is this.

Preventing Deadlocks

for a pseudo function like
void transaction(Account from, Account to, double amount){
Semaphore lock1, lock2;
lock1 = getLock(from);
lock2 = getLock(to)
wait(lock1);
wait(lock2);
withdraw(from, amount);
deposit(to, amount);
signal(lock2);
signal(lock1);
}
deadlock happens if you run transaction(A,B,50) transaction(B,A,10)
how can this be prevented?
would this work?
A simple deadlock prevention strategy when handling locks is to have strict order on the locks in the application and always grab the locks according to this order. Assuming all accounts have a number, you could change your logic to always grab the lock for the account with the lowest account number first. Then grab the lock for the one with the highest number.
Another strategy for preventing deadlocks is to reduce the number of locks. In this case it might be better to have one lock that locks all accounts. It would definitely make the lock structure far more simple. If the application shows performance problems under heavy load and profiling shows that lock congestion is the problem - then it is time to invent a more fine grained locking strategy.
By making the entire transaction a critical section? That's only one possible solution, at least.
I have a feeling this is homework of some sort, because it's very similar to the dining philosophers problem based on the example code you give. (Multiple solutions to the problem are available at the link provided, just so you know. Check them out if you want a better understanding of the concepts.)

How to serialize data reliably

Good day, I receive data from a communication channel and display it. Parallel, I serialize it into a SQLite database (using normal SQL INSERT statements). After my application exit I do a .commit on the sqlite object.
What happens if my application is terminated brutally in the middle? Will the latest (reasonably - not say 100 microsec ago, but at least a sec ago) data be safely in the database even without a .commit is made? Or should I have periodic commit? What are best patterns for doing these things?
I tried autocommit on (sqlite's option) and this slows code a lot by a factor ~55 (autocommit vs. just one commit at end). Doing commit every 100 inserts brings performance within 20% of the optimal mode. So autocommit is very slow for me.
My application pumps lots data into DB - what can I do to make it work well?
You should be performing this within a transaction, and consequently performing a commit at appropriate points in the process. A transaction will guarantee that this operation is atomic - that is, it either works or doesn't work.
Atomicity states that database
modifications must follow an “all or
nothing” rule. Each transaction is
said to be “atomic” if when one part
of the transaction fails, the entire
transaction fails. It is critical that
the database management system
maintain the atomic nature of
transactions in spite of any DBMS,
operating system or hardware failure.
If you've not committed, then the inserts won't be visible (and be rolled back) when your process is terminated.
When do you perform these commits ? When your inserts represent something consistent and complete. e.g.. if you have to insert 2 pieces of information for each message, then commit after you've inserted both pieces of info. Don't commit after each one, since your info won't be consistent or complete.
The data is not permanent in the database without a commit. Use an occasional commit to balance the speed of performing many inserts in a transaction (the more frequent the commit, the slower) with the safety of having more frequent commits.
You should do a COMMIT every time you complete a logical change.
One reason for transaction is to prevent uncommitted data from a transaction to be visible from outside. That is important because sometimes a single logical change can translate into multiple INSERT or UPDATE statements. If one of the latter queries of the transaction fails, the transaction can be cancelled with ROLLBACK and no change at all is recorded.
Generally speaking, no change performed in a transaction is recorded in the database until COMMIT succeeds.
does not this slow down considerably my code? – zaharpopov
Frequent commits, might slow down your code, and as an optimization you could try grouping several logical changes in a single transaction. But this is a departure from the correct use of transactions and you should only do this after measuring that this significantly improves performance.

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