Programming

yes? maybe, depending on what you mean.

Let's say you're doing a job and that job will involve reading 1M records or something. Pagination means you grab N number at a time, say 1000, in multiple queries as they're being done.

Reading your post again to try and get context, it looks like you're identifying duplicates as part of a job.

I don't know what you're using to determine a duplicate, if it's structural or not, but since you're running on HDDs, it might be faster to get that information into ram and then do the job in batches and update in batches. This will also allow you to do things like writing to the DB while doing CPU processing.

BTW, your hard disks are going to be your bottleneck unless you're reaching out over the internet, so your best bet is to move that data onto an NVMe SSD. That'll blow any other suggestion I have out of the water.

BUT! there are ways to help things out. I don't know what language you're working in. I'm a dotnet dev, so I can answer some things from that perspective.

One thing you may want to do, especially if there's other traffic on this server:

• use WITH (NOLOCK) so that you're not stopping other reads and write on the tables you're looking at
• use pagination, either with windowing or LIMIT/SKIP to grab only a certain number of records at a time

Use a HashSet (this can work if you have record types) or some other method of equality that's property based. Many Dictionary/HashSet types can take some kind of equality comparer.

So, what you can do is asynchronously read from the disk into memory and start some kind of processing job. If this job does also not require the disk, you can do another read while you're processing. Don't do a write and a read at the same time since you're on HDDs.

This might look something like:

offset = 0, limit = 1000

task = readBatchFromDb(offset, limit)

result = await task

data = new HashSet\<YourType>(new YourTypeEqualityComparer()) // if you only care about the equality and not the data after use, you can just store the hash codes

while (!result.IsEmpty) {

offset = advance(offset)

task = readBatchFromDb(offset, limit) // start a new read batch



dataToWork = data.exclusion(result) // or something to not rework any objects

data.addRange(result)



dataToWrite = doYourThing(dataToWork)

// don't write while reading

result = await task



await writeToDb(dataToWrite) // to not read and write. There's a lost optimization on not doing any cpu work

}



// Let's say you can set up a read or write queue to keep things busy

abstract class IoJob {

public sealed class ReadJob(your args) : IoJob

{

Task\<Data> ReadTask {get;set;}

}

public sealed class WriteJob(write data) : IoJob

{

Task WriteTask {get;set;}

}

}



Task\<IoJob> executeJob(IoJob job){

switch job {

ReadJob rj => readBatchFromDb(rj.Offset, rj.Limit), // let's say this job assigns the data to the ReadJob and returns it

WriteJob wj => writeToDb(wj) // function should return the write job

}

}



Stack\<IoJob> jobs = new ();



jobs.Enqueue(new ReadJob(offset, limit));

jobs.Enqueue(new ReadJob(advance(offset), limit)); // get the second job ready to start



job = jobs.Dequeue();

do () {

// kick off the next job

if (jobs.Peek() != null) executeJob(jobs.Peek());



if (result is ReadJob rj) {



data = await rj.Task;

if (data.IsEmpty) continue;



jobs.Enqueue(new ReadJob(next stuff))



dataToWork = data.exclusion(data)

data.AddRange(data)



dataToWrite = doYourThing(dataToWork)

jobs.Enqueue(new WriteJob(dataToWrite))

}

else if (result is WriteJob wj) {

await writeToDb(wj.Data)

}



} while ((job = jobs.Dequeue()) != null)