Clickhouse allows high performance writes but only if they are done in bulk and with intervals (recommended is at least 1 second interval between inserts). In the documentation to JDBC connector for Clickhouse batchSize option exists but there is nothing about intervals between inserts and I didn't manage to find insertion logic in the code (I am not a Java guy though).
Does it mean there are no intervals and Pulsar simply does inserts as frequently as it can?
I know nothing about Pulsar.
recommended is at least 1 second interval between inserts
That recommendation is "one insert per second".
Nothing about 1 second to sleep.
This recommendation is too basic and very vague.
Every project is unique, has own environment and requirements.
In one project I insert 10mil. very wide rows per minute with RAID 10 with HDD disks.
In another project I do 1000 inserts with ~100 narrow rows each per second using In-memory parts with single NVME disk.
Related
I'm running a job using Spring Batch 4.2.0 with postgres (11.2) as backend. It's all wrapped in a spring boot app. I've 5 steps and each runs using a simple partitioning strategy to divide data by id ranges and reads data into each partition (which are processed by separate threads). I've about 18M rows in the table, each step reads, changes few fields and writes back. Each step reads all 18M rows and writes back. The issue I'm facing is, the queries that run to pull data into each thread scans data by id range like,
select field_1, field_2, field_66 from table where id >= 1 and id < 10000.
In this case each thread processes 10_000 rows at a time. When there's no traffic the query takes less than a second to read all 10,000 rows. But when the job runs there's about 70 threads reading all that data in. It goes progressively slower to almost a minute and a half, any ideas where to start troubleshooting this?
I do see autovacuum running in the backgroun for almost the whole duration of job. It definitely has enough memory to hold all that data in memory (about 6GB max heap). Postgres has sufficient shared_buffers 2GB, max_wal_size 2GB but not sure if that in itself is sufficient. Another thing I see is loads of COMMIT queries hanging around when checking through pg_stat_activity. Usually as much as number of partitions. So, instead of 70 connections being used by 70 partitions there are 140 conections used up with 70 of them running COMMIT. As time progresses these COMMITs get progressively slower too.
You are probably hitting https://github.com/spring-projects/spring-batch/issues/3634.
This issue has been fixed and will be part of version 4.2.3 planned to be released this week.
I have been searching for an answer to this today, and it seems the best approach divides opinion somewhat.
I have 150,000 records that I need to retrieve from an Oracle database using JDBC. Is it better to retrieve the data using one select query and allowing the JDBC driver to take care of transferring the records from the database using Oracle cursor and default fetchSize - OR to split up the query into batches using LIMIT / OFFSET?
With the LIMIT / OFFSET option, I think the pros are that you can take control over the number of results you return in each chunk. The cons are that the query is executed multiple times, and you also need to run a COUNT(*) up front using the same query to calculate the number of iterations required.
The pros of retrieving all at once are that you rely on the JDBC driver to manage the retrieval of data from the database. The cons are that the setFetchSize() hint can sometimes be ignored meaning that we could end up with a huge resultSet containing all 150,000 records at once!!
Would be great to hear some real life experiences solving similar issues, and recommendations would be much appreciated.
The native way in Oracle JDBC is to use the prepareStatement for the query, executeQuery and fetch
in a loop the results with defined fetchSize
Yes, of course the details are Oracle Database and JDBC Driver Version dependent and in some case the required fetchSize
can be ignored. But the typical problem is that the required fetch size is reset to fetchSize = 1 and you effectively makes a round trip for each record. (not that you get all records at once).
Your alternative with LIMIT seems to be meaningfull on the first view. But if you investigate the implementation you will probably decide to not use it.
Say you will divide the result set in 15 chunks 10K each:
You open 15 queries, each of them on average with a half of the resource consumption as the original query (OFFSET select the data and skips them).
So the only think you will reach is that the processing will take aproximatly 7,5x more time.
Best Practice
Take your query, write a simple script with JDBC fetch, use 10046 trace to see the effective used fetch size.
Test with a range of fetch sizes and observe the perfomance; choose the optimal one.
my preference is to maintain a safe execution time with the ability to continue if interrupted. i prefer this approach because it is future proof and respects memory and execution time limits. remember you're not planning for today, you're planning for 6m down the road. what may be 150,000 today may be 1.5m in 6 months.
i use a length + 1 recipe to know if there is more to fetch, although the count query will enable you to do a progress bar in % if that is important.
when considering 150,000 record result set, this is a memory pressure question. this will depend on the average size of each row. if it is a row with three integers, that's small. if it is a row with a bunch of text elements to store user profile details then that's potentially very large. so be prudent with what fields you're pulling.
also need to ask - you may not need to pull all the records all the time. it may be useful to apply a sync pattern. to only pull records with an updated date newer than your last pull.
I am looking for a distributed solution to screen/filter a large volume of keys in real-time. My application generates over 100 billion records per day, and I need a way to filter duplicates out of the stream. I am looking for a system to store a rolling 10 days’ worth of keys, at approximately 100 bytes per key. I was wondering how this type of large scale problem has been solved before using Hadoop. Would HBase be the correct solution to use? Has anyone ever tried a partially in-memory solution like Zookeeper?
I can see a number of solutions to your problem, but the real-time requirement really narrows it down. By real-time do you mean you want to see if a key is a duplicate as its being created?
Let's talk about queries per second. You say 100B/day (that's a lot, congratulations!). That's 1.15 Million queries per second (100,000,000,000 / 24 / 60 / 60). I'm not sure if HBase can handle that. You may want to think about something like Redis (sharded perhaps) or Membase/memcached or something of that sort.
If you were to do it in HBase, I'd simply push the upwards of a trillion keys (10 days x 100B keys) as the keys in the table, and put some value in there to store it (because you have to). Then, you can just do a get to figure out if the key is in there. This is kind of hokey and doesn't fully utilize hbase as it is only fully utilizing the keyspace. So, effectively HBase is a b-tree service in this case. I don't think this is a good idea.
If you relax the restraint to not have to do real-time, you could use MapReduce in batch to dedup. That's pretty easy: it's just Word Count without the counting. You group by the key you have and then you'll see the dups in the reducer if multiple values come back. With enough nodes an enough latency, you can solve this problem efficiently. Here is some example code for this from the MapReduce Design Patterns book: https://github.com/adamjshook/mapreducepatterns/blob/master/MRDP/src/main/java/mrdp/ch3/DistinctUserDriver.java
ZooKeeper is for distributed process communication and synchronization. You don't want to be storing trillions of records in zookeeper.
So, in my opinion, you're better served by a in-memory key/value store such as redis, but you'll be hard pressed to store that much data in memory.
I am afraid that it is impossible with traditional systems :|
Here is what U have mentioned:
100 billion per days means approximation 1 million per second!!!!
size of the key is 100 bytes.
U want to check for duplicates in a 10 day working set means 1 trillion items.
These assumptions results in look up in a set of 1 trillion objects that totally size in 90 TERABYTES!!!!!
Any solution to this real-time problem shall provide a system that can look up 1 million items per second in this volume of data.
I have some experience with HBase, Cassandra, Redis, and Memcached. I am sure that U cannot achieve this performance on any disk-based storage like HBase, Cassandra, or HyperTable (and add any RDBMSs like MySQL, PostgreSQl, and... to these). The best performance of redis and memcached that I have heard practically is around 100k operations per second on a single machine. This means that U must have 90 machines each having 1 TERABYTES of RAM!!!!!!!!
Even a batch processing system like Hadoop cannot do this job in less than an hour and I guess it will take hours and days on even a big cluster of 100 machines.
U R talking about very very very big numbers (90 TB, 1M per second). R U sure about this???
Given: SQL Server 2008 R2. Quit some speedin data discs. Log discs lagging.
Required: LOTS LOTS LOTS of inserts. Like 10.000 to 30.000 rows into a simple table with two indices per second. Inserts have an intrinsic order and will not repeat, as such order of inserts must not be maintained in short term (i.e. multiple parallel inserts are ok).
So far: accumulating data into a queue. Regularly (async threadpool) emptying up to 1024 entries into a work item that gets queued. Threadpool (custom class) has 32 possible threads. Opens 32 connections.
Problem: performance is off by a factor of 300.... only about 100 to 150 rows are inserted per second. Log wait time is up to 40% - 45% of processing time (ms per second) in sql server. Server cpu load is low (4% to 5% or so).
Not usable: bulk insert. The data must be written as real time as possible to the disc. THis is pretty much an archivl process of data running through the system, but there are queries which need access to the data regularly. I could try dumping them to disc and using bulk upload 1-2 times per second.... will give this a try.
Anyone a smart idea? My next step is moving the log to a fast disc set (128gb modern ssd) and to see what happens then. The significant performance boost probably will do things quite different. But even then.... the question is whether / what is feasible.
So, please fire on the smart ideas.
Ok, anywering myself. Going to give SqlBulkCopy a try, batching up to 65536 entries and flushing them out every second in an async fashion. Will report on the gains.
I'm going through the exact same issue here, so I'll go through the steps i'm taking to improve my performance.
Separate the log and the dbf file onto different spindle sets
Use basic recovery
you didn't mention any indexing requirements other than the fact that the order of inserts isn't important - in this case clustered indexes on anything other than an identity column shouldn't be used.
start your scaling of concurrency again from 1 and stop when your performance flattens out; anything over this will likely hurt performance.
rather than dropping to disk to bcp, and as you are using SQL Server 2008, consider inserting multiple rows at a time; this statement inserts three rows in a single sql call
INSERT INTO table VALUES ( 1,2,3 ), ( 4,5,6 ), ( 7,8,9 )
I was topping out at ~500 distinct inserts per second from a single thread. After ruling out the network and CPU (0 on both client and server), I assumed that disk io on the server was to blame, however inserting in batches of three got me 1500 inserts per second which rules out disk io.
It's clear that the MS client library has an upper limit baked into it (and a dive into reflector shows some hairy async completion code).
Batching in this way, waiting for x events to be received before calling insert, has me now inserting at ~2700 inserts per second from a single thread which appears to be the upper limit for my configuration.
Note: if you don't have a constant stream of events arriving at all times, you might consider adding a timer that flushes your inserts after a certain period (so that you see the last event of the day!)
Some suggestions for increasing insert performance:
Increase ADO.NET BatchSize
Choose the target table's clustered index wisely, so that inserts won't lead to clustered index node splits (e.g. autoinc column)
Insert into a temporary heap table first, then issue one big "insert-by-select" statement to push all that staging table data into the actual target table
Apply SqlBulkCopy
Choose "Bulk Logged" recovery model instad of "Full" recovery model
Place a table lock before inserting (if your business scenario allows for it)
Taken from Tips For Lightning-Fast Insert Performance On SqlServer
I have a very large set of data (~3 million records) which needs to be merged with updates and new records on a daily schedule. I have a stored procedure that actually breaks up the record set into 1000 record chunks and uses the MERGE command with temp tables in an attempt to avoid locking the live table while the data is updating. The problem is that it doesn't exactly help. The table still "locks up" and our website that uses the data receives timeouts when attempting to access the data. I even tried splitting it up into 100 record chunks and even tried a WAITFOR DELAY '000:00:5' to see if it would help to pause between merging the chunks. It's still rather sluggish.
I'm looking for any suggestions, best practices, or examples on how to merge large sets of data without locking the tables.
Thanks
Change your front end to use NOLOCK or READ UNCOMMITTED when doing the selects.
You can't NOLOCK MERGE,INSERT, or UPDATE as the records must be locked in order to perform the update. However, you can NOLOCK the SELECTS.
Note that you should use this with caution. If dirty reads are okay, then go ahead. However, if the reads require the updated data then you need to go down a different path and figure out exactly why merging 3M records is causing an issue.
I'd be willing to bet that most of the time is spent reading data from the disk during the merge command and/or working around low memory situations. You might be better off simply stuffing more ram into your database server.
An ideal amount would be to have enough ram to pull the whole database into memory as needed. For example, if you have a 4GB database, then make sure you have 8GB of RAM.. in an x64 server of course.
I'm afraid that I've quite the opposite experience. We were performing updates and insertions where the source table had only a fraction of the number of rows as the target table, which was in the millions.
When we combined the source table records across the entire operational window and then performed the MERGE just once, we saw a 500% increase in performance. My explanation for this is that you are paying for the up front analysis of the MERGE command just once instead of over and over again in a tight loop.
Furthermore, I am certain that merging 1.6 million rows (source) into 7 million rows (target), as opposed to 400 rows into 7 million rows over 4000 distinct operations (in our case) leverages the capabilities of the SQL server engine much better. Again, a fair amount of the work is in the analysis of the two data sets and this is done only once.
Another question I have to ask is well is whether you are aware that the MERGE command performs much better with indexes on both the source and target tables? I would like to refer you to the following link:
http://msdn.microsoft.com/en-us/library/cc879317(v=SQL.100).aspx
From personal experience, the main problem with MERGE is that since it does page lock it precludes any concurrency in your INSERTs directed to a table. So if you go down this road it is fundamental that you batch all updates that will hit a table in a single writer.
For example: we had a table on which INSERT took a crazy 0.2 seconds per entry, most of this time seemingly being wasted on transaction latching, so we switched this over to using MERGE and some quick tests showed that it allowed us to insert 256 entries in 0.4 seconds or even 512 in 0.5 seconds, we tested this with load generators and all seemed to be fine, until it hit production and everything blocked to hell on the page locks, resulting in a much lower total throughput than with the individual INSERTs.
The solution was to not only batch the entries from a single producer in a MERGE operation, but also to batch the batch from producers going to individual DB in a single MERGE operation through an additional level of queue (previously also a single connection per DB, but using MARS to interleave all the producers call to the stored procedure doing the actual MERGE transaction), this way we were then able to handle many thousands of INSERTs per second without problem.
Having the NOLOCK hints on all of your front-end reads is an absolute must, always.