I have a requirement where I have to select around 60 million plus records from database. Once I have all records in ResultSet then I have to formate some columns as per the client requirement(date format and number format) and then I have to write all records in a file(secondary memory).
Currently I am selecting records on day basis (7 selects for 7 days) from DB and putting them in a HashMap. Reading from HashMap and formating some columns and finally writing in a file(separate file for 7 days).
Finally I am merging all 7 files in a single file.
But this whole process is taking 6 hrs to complete. To improve this process I have created 7 threads for 7 days and all threads are writing separate files.
Finally I am merging all 7 files in a single file. This process is taking 2 hours. But my program is going to OutOfMemory after 1 hour and so.
Please suggest the best design for this scenario, should I used some caching mechanism, if yes, then which one and how?
Note: Client doesn't want to change anything at Database like create indexes or stored procedures, they don't want to touch database.
Thanks in advance.
Do you need to have all the records in memory to format them? You could try and stream the records through a process and right to the file. If your able to even break the query up further you might be able to start processing the results, while your still retrieving them.
Depending on your DB backend they might have tools to help with this such as SSIS for Sql Server 2005+.
Edit
I'm a .net developer so let me suggest what I would do in .net and hopefully you can convert into comparable technologies on the java side.
ADO.Net has a DataReader which is a forward only, read only (Firehose) cursor of a resultset. It returns data as the query is executing. This is very important. Essentially, my logic would be:
IDataReader reader=GetTheDataReader(dayOfWeek);
while (reader.Read())
{
file.Write(formatRow(reader));
}
Since this is executing while we are returning rows your not going to block on the network access which I am guessing is a huge bottleneck for you. The key here is we are not storing any of this in memory for long, as we cycle the reader will discard the results, and the file will write the row to disk.
I think what Josh is suggesting is this:
You have loops, where you currently go through all the result records of your query (just using pseudo code here):
while (rec = getNextRec() )
{
put in hash ...
}
for each rec in (hash)
{
format and save back in hash ...
}
for each rec in (hash)
{
write to a file ...
}
instead, do it like this:
while (rec = getNextRec() )
{
format fields ...
write to the file ...
}
then you never have more than 1 record in memory at a time ... and you can process an unlimited number of records.
Obviously reading 60 million records at once is using up all your memory - so you can't do that. (ie your 7 thread model). Reading 60 millions records one at a time is using up all your time - so you can't do that either (ie your initial read to file model).
So.... you're going to have to compromise and do a bit of both.
Josh has it right - open a cursor to your DB that simply reads the next record, one after the other in the simplest, most feature-light way. A "firehose" cursor (otherwise known as a read-only, forward-only cursor) is what you want here as it imposes the least load on the database. The DB isn't going to let you update the records, or go backwards in the recordset, which you don't want anyway, so it won't need to handle memory for the records.
Now you have this cursor, you're being given 1 record at a time by the DB - read it, and write it to a file (or several files), this should finish quite quickly. Your task then is to merge the files into 1 with the correct order, which is relatively easy.
Given the quantity of records you have to process, I think this is the optimum solution for you.
But... seeing as you're doing quite well so far anyway, why not just reduce the number of threads until you are within your memory limits. Batch processing is run overnight is many companies, this just seems to be another one of those processes.
Depends on the database you are using, but if it was SQL Server, I would recommend using something like SSIS to do this rather than writing a program.
Related
Background I am using SQLite to store around 10M entries, where the size of each entry is around 1Kb. I am reading this data back in chunks of around 100K entries at a time, using multiple parallel threads. Read and writes are not going in parallel and all the writes are done before starting the reads.
Problem I am experiencing too many disk reads. Each second around 3k reads are happening and I am reading only 30Kb data in those 3k reads (Hence around 100 bytes per disk read). As the result, I am seeing a really horrible performance (It is taking around 30 minutes to read the data)
Question
Is there any SQlite settings/pragmas that I can use to avoid the small size disk reads?
Are there any best practices for batch parallel reads in SQlite?
Does SQlite read all the results of a query in one go? Or read the results in smaller chunks? If latter is the case, then where does it stone partial out of a query
Implementation Details My using SQlite with Java and my application is running on linux. JDBC library is https://github.com/xerial/sqlite-jdbc (Version 3.20.1).
P.S I am already built the necessary Indexes and verified that no table scans are going on (using Explain Query planner)
When you are searching for data with an index, the database first looks up the value in the index, and then goes to the corresponding table row to read all the other columns.
Unless the table rows happen to be stored in the same order as the values in the index, each such table read must go to a different page.
Indexes speed up searches only if the seach reduces the number of rows. If you're going to read all (or most of the) rows anyway, a table scan will be much faster.
Parallel reads will be more efficient only if the disk can actually handle the additional I/O. On rotating disks, the additional seeks will just make things worse.
(SQLite tries to avoid storing temporary results. Result rows are computed on the fly (as much as possible) while you're stepping through the cursor.)
We have the background job which fetches records from particular table in batch of 1000 records at a time.
This job runs at a interval of 30 minutes.
Now,
These records have email (key) and reason (value).
The issues is we have to lookup these records against data warehouse, (sort of filtering kind of thing - fetches last 180 days data from warehouse).
As call to the data warehouse is very costly in terms of time (45 minutes approximately).
So, existing scenario is like this.
We check for flat file on disk. if it does not exists. we make a call to data warehouse, fetch the records ( size ranges from 0.2 million to 0.25 million )
and write these records on a disk.
on subsequent calls we do lookup from flat file only.
- Loading entire file in memory and do in-memory search and filtering.
This caused OutOfMemory issue so many times.
So, we modified the logic like this.
We modified the Data warehouse call in 4 equal intervals and stored each result in file again with ObjectOutputStream,
Now, on subsequent calls we load data into memory in interval, i.e. 0-30 days, 31-60 days and so on.
But, this also is not helping out. Can experts please suggest what should be the ideal way to tackle this issue ?. Someone in senior team suggested to use CouchDB for storing and querying the data. But, at first glance I would prefer if with existing infrastructure any good solution is available ? if not then can think of using other tools.
Filtering code as of now.
private void filterRecords(List<Record> filterRecords) {
long start = System.currentTimeMillis();
logger.error("In filter Records - start (ms) : "+start);
Map<String, String> invalidDataSet=new HashMap<String, String>(5000);
try{
boolean isValidTempResultFile = isValidTempResultFile();
//1. fetch invalid leads data from DWHS only if existing file is 7 days older.
String[] intervals = {"0","45","90","135"};
if(!isValidTempResultFile){
logger.error("#CCBLDM isValidTempResultFile false");
for(String interval : intervals){
invalidDataSet.clear();
// This call takes approx 45 minutes to fetch the data
getInvalidLeadsFromDWHS(invalidDataSet, interval, start);
filterDataSet(invalidDataSet, filterRecords);
}
}
else{
//Set data from temporary file in interval to avoid heap space issue
logger.error("#CCBLDM isValidTempResultFile true");
intervals = new String[]{"1","2","3","4"};
for(String interval : intervals){
// Here GC won't happen at immediately causing OOM issue.
invalidDataSet.clear();
// Keeps 45 days of data in memory at a time
setInvalidDataSetFromTempFile(invalidDataSet, interval);
//2. mark current record as incorrect if it belongs to invalid email list
filterDataSet(invalidDataSet, filterRecords);
}
}
}catch(Exception filterExc){
Scheduler.log.error("Exception occurred while Filtering Records", filterExc);
}finally{
invalidDataSet.clear();
invalidDataSet = null;
}
long end = System.currentTimeMillis();
logger.error("Total time taken to filter all records ::: ["+(end-start)+"] ms.");
}
I'd strongly suggest to slightly change your infrastructure. IIYC you're looking up something in a file and something in a map. Working with a file is a pain and loading everything to memory causes OOME. You can do better using a memory mapped file, which allows fast and simple access.
There's a Chronicle-Map offering a Map interface to data stored off-heap. Actually, the data reside on the disk and take main memory as needed. You need to make your keys and values Serializable (which AFAIK you did already) or use an alternative way (which may be faster).
It's no database, it's just a ConcurrentMap, which makes working with it very simple. The whole installation is just adding a line like compile "net.openhft:chronicle-map:3.14.5" to build.gradle (or a few maven lines).
There are alternatives, but Chronicle-Map is what I've tried (just started with it, but so far everything works perfectly).
There's also Chronicle-Queue, in case you need batch processing, but I strongly doubt you'll need it as you're limited by your disk rather than main memory.
This is a typical use case for a batch processing kind of code. You could introduce a new column, say 'isProcessed' with 'false' value. Read say 1-100 rows (where id>=1 && id<=100) , process them and mark that flag as true. Then read say next 100 and so on. At the end of the job, mark all flags as false again (reset). But with time, it might become difficult to maintain and develop features on such a custom framework. There are open source alternatives.
Spring batch is a very good framework for these use cases and could be considered: https://docs.spring.io/spring-batch/trunk/reference/html/spring-batch-intro.html.
There is also Easy batch: https://github.com/j-easy/easy-batch which doesn't require 'Spring framework' knowledge
But if it is a huge data set and would keep on growing in future, consider moving to 'big data' tech stack
I have around 15 million records in MySQL (read only) which will be fetched using joins of 10 tables. Around 50000 new records are inserted daily. Number will keep on increasing in future.
Each record will be processed independently by a java program. Multiple processing will be done on the same record and output will be calculated based on the processing.
Results will be stored in another database.
Processing shall be completed within an hour
My questions are
How to design the processing engine (cluster of java programs) in a distributed manner making the processing as fast as possible? To be more precise, I want to boot many spot instance at that time and finish the processing.
Will mysql be a read bottleneck?
I don't have any experience in big data solutions. Shall I use spark or any other map reduce solution? If yes, then how shall I proceed?
I was in a similar situation where we were collecting about 15 million records per day. What I did was create some collection tables that I rotated and performed initial processing. Once that was done, I moved the data to the next phase where further processing was done before adding it to the large collection of data. Breaking it down will get the best performance and avoid having to run through a large set of data.
I'm not sure what you mean about processing data and why you want to do it in Java, you may have a good reason for that. I would imagine that performance would be much better if you offload that to MySQL and let it do as much of the processing as possible.
Now, I made a sound searching program.
First, I saved the file path (sound file) in the MySQL DB, then recorded the sound and searched for a matching file comparing the sound fingerprint. But it takes long, because I have a lot of rows (sound files) in the DB. So, here are several questions.
I want to connect to the MySQL database to get information using Java
To increase the speed of the program, I want to use multithreading.
How can I do that?
(For example, I want to make the first thread query the first 10 rows,
and the second the next 10 rows. Approximately, the table has more than
500 rows.)
How can I compare the result of the threads? Can each thread return a value?
TO second point about thread job results you can use Callable interface and ExecutionService ( are build-in in standard library)
Try this example :
https://blogs.oracle.com/CoreJavaTechTips/entry/get_netbeans_6
Here is the scenario I am researching a solution for at work. We have a table in postgres which stores events happening on network. Currently the way it works is, rows get inserted as network events come and at the same time older records which match the specific timestamp get deleted in order to keep table size limited to some 10,000 records. Basically, similar idea as log rotation. Network events come in burst of thousands at a time, hence rate of transaction is too high which causes performance degradation, after sometime either server just crashes or becomes very slow, on top of that, customer is asking to keep table size up to million records which is going to accelerate performance degradation (since we have to keep deleting record matching specific timestamp) and cause space management issue. We are using simple JDBC to read/write on table. Can tech community out there suggest better performing way to handle inserts and deletes in this table?
I think I would use partitioned tables, perhaps 10 x total desired size, inserting into the newest, and dropping the oldest partition.
http://www.postgresql.org/docs/9.0/static/ddl-partitioning.html
This makes load on "dropping oldest" much smaller than query and delete.
Update: I agree with nos' comment though, the inserts/deletes may not be your bottleneck. Maybe some investigation first.
Some things you could try -
Write to a log, have a separate batch proc. write to the table.
Keep the writes as they are, do the deletes periodically or at times of lower traffic.
Do the writes to a buffer/cache, have the actual db writes happen from the buffer.
A few general suggestions -
Since you're deleting based on timestamp, make sure the timestamp is indexed. You could also do this with a counter / auto-incremented rowId (e.g. delete where id< currentId -1000000).
Also, JDBC batch write is much faster than individual row writes (order of magnitude speedup, easily). Batch writing 100 rows at a time will help tremendously, if you can buffer the writes.