I use snowflake for turning out if it can use for DWH, and I am concern with the query behavior when the warehouse somehow fails.
https://docs.snowflake.com/en/user-guide/warehouses-considerations.html#multi-cluster-warehouses-improve-concurrency
According to the above page, if the minimum cluster is set to higher than 1, it helps ensure availability and continuity.
I have questions about it.
1.If we set it to 1 and the warehouse fails, the proceeded query come to fail?
2.If we set it to 2 or more and a cluster of the warehouse fails, the proceeded query come to fail and start automatically by another cluster?
When a warehouse fails, a new warehouse is automatically started and the query is retired. In the 6 years at my prior job that we ran snowflake, there was less that a dozen times where we experienced warehouse failure.
It was often around releases being rolled out. One thing that does happen on failure is the release is pushed back. So we noticed blips in our processing rate, or increased total time, and during the trouble periods the query profile might show 1-3 tabs of query plan, for each retry.
At least one of those outages was a failure to bring up new warehouses, class of problem, and in that incident, I don't think we where impacted as we have stuff just always running.
A side note also is you get billed for those failures, so that can bite if you are doing large computations and it fails and retries. We have had refunds (of the extra cost) when we can show there was a cost increase due to a known failure event.
But if you are looking at run Medium and smaller warehouses those normally start same second, so you might not notice a "failure" but if you are running a really large instance size, it can take longer to bring that capacity online.
Related
I have been using Clickhouse at work for analytics purposes for a while now.
I am currently running Clickhouse v22.6.3 revision 54455 on-premise on a VM with:
fast storage
200Gb of RAM
no swap
a 40-cores CPU.
I have a few Tb of data, but no table bigger than 300 Gb. I do not use distributed tables or replication yet, and I write frequently into Clickhouse (but I don't use deletes or updates and prefer using things like the ReplacingMergeTree engine). I also leverage the MaterializedView feature for a few tables. Let me know if you need any more context or parameter, I use a pretty standard configuration.
Now, for a few months I have been experiencing performances issues where the server significantly slows down every day at 10am, and I cannot figure out why.
Based on Clickhouse built-in Graphite monitoring, the "symptoms" of the issue seem to be as follow:
At 10am:
On the server side:
Both load and RAM usage remain reasonable. Load goes up a little.
Disk write await time goes up (which I suspect is what leads to higher load)
Disk utilization % skyrockets to something between 90 and 100%
On Clickhouse side:
DiskSpaceReservedForMerge stays roughly the same (ie between 0 and 70Gb)
both OpenFileForRead and OpenFileForWrite go up by a factor of ~2
BackgroundCommonPoolTask goes slightly up, so does BackgroundSchedulePoolTask (which I found weird, because I thought this pool was dedicated to distributed operations - which I don't use) - both numbers remain seemingly reasonable
The number of active Merge tasks per minutes drop significantly but I'm unsure whether it's a consequence of slow writing or if it's causing it
both insert and general querying time are multiplied by ~10 which renders the database effectively unusable even for small tasks
Restarting Clickhouse usually fixes the problem but I obviously do not want to restart my main database every day at 10am. Most of the heavy load I put on the DB (such as data extraction and transformation, etc) happens earlier in the morning (and end around 7-8am) and runs fine. I do not have any heavy tasks running at 10am. The Clickhouse VM takes most of its host resources and I have confirmed with the devOps team that there doesn't seem to be a problem on the host or anything else scheduled on it at that time.
Is there any kind of background tasks or process that is run by Clickhouse on a daily basis and that could have a high impact on our disk capacity? What else can I monitor to figure out what is causing this problem?
Again, let me know if I can be more thorough on our settings and the state of the DB when the "bug" occurs.
Do you use https://github.com/innogames/graphite-ch-optimizer ?
Do you use TTL ?
select * from system.merges;
select * from system.part_log where event_time between ~10am~
I have a web service sitting on IIS that has been quite happy for months but now I'm getting timeouts and I don't know how to diagnose what the problem is.
The client sends up basic information in a 'heartbeat' message to IIS which then updates this in a SQL database (on a different server). There are 250 clients in the wild, all sending up their heartbeat every 5 minutes ... so there's only 250 rows in the table, with appropriate indexing on the column being used for the update.
Ordinarily it only takes 50-100ms to do the update, but since last week you can see that the response time in the IIS log has increased and I'm also getting timeouts too.
Nothing has changed with the setup so I don't know what I'm looking for to determine the reason. The error I get back is:
System.ServiceModel.FaultException: An error occurred while updating
the entries. See the inner exception for details.An error occurred
while updating the entries. See the inner exception for
details.Execution Timeout Expired. The timeout period elapsed prior to
completion of the operation or the server is not responding. The
statement has been terminated.The wait operation timed out
Any advice on where to start looking? I did enable the failed request log trace in IIS but I don't know what it all means if I'm perfectly honest. The difference between a successful requiest and a failed one is that the request log stops after the 'AspNetStart' entry.
Thanks!
Mark
There are lots of reasons a service can gradually or suddenly become slow. Poor code structure can lead to things like memory leaks on the server, small enough they don't really show up or cause problems during testing, but when run over weeks/months start to stack up. Unauthorized requests could be targeting your server if this is a public-facing service, or has a link to public-facing services.
Things to look at:
Does this happen at certain times of the day or throughout the day?
Is this a load issue that starts occurring when multiple users are sending updates concurrently? 250 users isn't a lot. Has the # of users grown over the last few months or has it been relatively stable since the start?
What is the memory and CPU usage looking like on the Web server(s) and DB server?
This is the first clue to check to see if either server is under considerable load. From there you can investigate why it might be under load or if it possibly needs a bit more grunt to deal with the load. Look at the running processes. If these servers are managed by an IT department or such some culprits can include things like Virus Scanners hogging resources. (I.e. policy changes in the last few months have lead to additional load on the servers)
What recovery model is your database set up for?
What is the size of your Tx Log (.mdx file)
Do you have a regular scheduled database backup and index maintenance?
This is one that new projects tend to forget. An empty database is small and has no Tx Log history being recorded, but as it runs over time that Tx Log grows silently in the background, especially with Full recovery. Larger Tx Logs can lead to slower performance over time especially if the log file needs to be enlarged. A good thing to check is whether the log file is set to grow by a # of bytes or percentage. Percentage is I believe the default but this can cause exponential "grow" time/space issues so it's better to set it to a fixed size per grow. You'll want regular backups being done that allow the Tx Log to reset. Ideally don't shrink the file if the Log size between backups stays consistent.
How many records across all tables are being inserted or updated in a given day?
This is important to build a picture of how much the database will be tracking through the day between backups. You may have 250 clients, but every heartbeat is potentially updating a row and inserting others.
What are you using for PKs for inserted records? (Ints vs. UUIDs) If using UUIDs are you using NEWSEQUENTIALID() or NEWID()/Guid.New()?
GUIDs can be a time bomb for indexing if done poorly. A GUID combined with NEWID() or Guid.New() will lead to considerable index fragmentation when inserting rows. Provided the GUIDs are not visible to clients you should use NEWSEQUENTIALID(). If IDs are set via code then there are implementations you can find to generate sequential GUIDs. (It's a matter of re-arranging the parts that make up the GUID) Regular index maintenance is a requirement for using UUID columns in indexed fields.
Are you using Dependency Injection in your web service?
What is the lifetime scope of the DbContexts performing the updates?
This is a potential time bomb for web servers if the lifetime scope for a DbContext is set up incorrectly. You want a DbContext to be alive for no longer than it is needed. At a maximum the lifetime scope should be set to PerRequest. A DbContext set up for Singleton for instance would be tracking entities across requests. The more entities a DbContext is tracking, the slower read and update operations become. This would be a possible culprit if the web server memory usage is climbing.
Are you running an SQL Profiler?
In a test environment with nothing else touching the database, running scenarios through the application with an SQL Profiler can reveal potential issues such as unexpected queries being kicked off due to things like lazy loading. For one operation you might expect one or a small number of queries to be run, only to find dozens or even hundreds. Multiply this across concurrent requests and you have a recipe for the database server to say "Just sit down and wait, dammit!" :) Any queries you don't expect based on the code that is running should be investigated for either eager loading relationships or implementing projection. (Recommended for best performance)
Do the web servers get restarted periodically?
For some tricky to debug issues and memory leaks, sometimes the easiest "fix" is to schedule regular restarts of the web server. It's a hack, but compared to the considerable cost of trying to track down memory leaks or fix up inefficient code that slows down over time, it is a cheap and effective fix. (At least while you do research options to address the issues and optimize the code)
That should give you a start into things to check with the service & database.
My team and I have been using Snowflake daily for the past eight months to transform/enrich our data (with DBT) and make it available in other tools.
While the platform seems great for heavy/long running queries on large datasets and powering analytics tools such as Metabase and Mode, it just doesnt seem to behave well in cases where we need to run really small queries (grab me one line of table A) behind a high demand API, what I mean by that is that SF sometimes takes as much as 100ms or even 300ms on a XLARGE-2XLARGE warehouse to fetch one row in a fairly small table (200k computed records/aggregates), that added up to the network latency makes for a very poor setup when we want to use it as a backend to power a high demand analytics API.
We've tested multiple setups with Nodejs + Fastify, as well as Python + Fastapi, with connection pooling (10-20-50-100)/without connection pooling (one connection per request, not ideal at all), deployed in same AWS region as our SF deployment, yet we werent able to sustain something close to 50-100 Requests/sec with 1s latency (acceptable), but rather we were only able to get 10-20 Requests/sec with as high as 15-30s latency. Both languages/frameworks behave well on their own, or even with just acquiring/releasing connections, what actually takes the longest and demands a lot of IO is the actual running of queries and waiting for a response. We've yet to try a Golang setup, but it all seems to boil down to how quick Snowflake can return results for such queries.
We'd really like to use Snowflake as database to power a read-only REST API that is expected to have something like 300 requests/second, while trying to have response times in the neighborhood 1s. (But are also ready to accept that it was just not meant for that)
Is anyone using Snowflake in a similar setup? What is the best tool/config to get the most out of Snowflake in such conditions? Should we spin up many servers and hope that we'll get to a decent request rate? Or should we just copy transformed data over to something like Postgres to be able to have better response times?
I don't claim to be the authoritative answer on this, so people can feel free to correct me, but:
At the end of the day, you're trying to use Snowflake for something it's not optimized for. First, I'm going to run SELECT 1; to demonstrate the lower-bound of latency you can ever expect to receive. The result takes 40ms to return. Looking at the breakdown that is 21ms for the query compiler and 19ms to execute it. The compiler is designed to come up with really smart ways to process huge complex queries; not to compile small simple queries quickly.
After it has its query plan it must find worker node(s) to execute it on. A virtual warehouse is a collection of worker nodes (servers/cloud VMs), with each VW size being a function of how many worker nodes it has, not necessarily the VM size of each worker (e.g. EC2 instance size). So now the compiled query gets sent off to a different machine to be run where a worker process is spun up. Similar to the query planner, the worker process is not likely optimized to run small queries quickly, so the spin-up and tear-down of that process might be involved (at least relative to say a PostgreSQL worker process).
Putting my SELECT 1; example aside in favor of a "real" query, let's talk caching. First, Snowflake does not buffer tables in memory the same way a typical RDBS does. RAM is reserved for computation resources. This makes sense since in traditional usage you're dealing with tables many GBs to TBs in size, so there would be no point since a typical LRU cache would purge that data before it was ever accessed again anyways. This means that a trip to an SSD disk must occur. This is where your performance will start to depend on how homogeneous/heterogeneous your API queries are. If you're lucky you get a cache hit on SSD, otherwise its off to S3 to get your tables. Table files are not redundantly cached across all worker nodes, so while the query planner will make an attempt to schedule a computation on a node most likely to have the needed files in cache, there is no guarantee that a subsequent query will benefit from the cache resulting from the first query if it is assigned to a different worker node. The likeliness of this happening increases if you're firing 100s of queries at the VM/second.
Lastly, and this could be the bulk of your problem but have saved it for last since I am the least certain on it. A small query can run on a subset of the workers in a virtual warehouse. In this case the VH can run concurrent queries with different queries on different nodes. BUT, I am not sure if a given worker node can process more than one query at once. In that case, your concurrency will be limited by the number of nodes in the VH, e.g. a VH with 10 worker nodes can at most run 10 queries in parallel, and what you're seeing are queries piling up at the query planner stage while it waits for worker nodes to free up.
maybe for this type of workload , the new SF feature Search Optimization Service could help you speeding up performances ( https://docs.snowflake.com/en/user-guide/search-optimization-service.html ).
I have to agree with #Danny C - that Snowflake is NOT designed for very low (sub-second) latency on single queries.
To demonstrate this consider the following SQL statements (which you can execute yourself):
create or replace table customer as
select *
from SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER
limit 500000;
-- Execution time 840ms
create or replace table customer_ten as
select *
from SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER
limit 10;
-- Execution time 431ms
I just ran this on an XSMALL warehouse and it demonstrates currently (November 2022) Snowflake can copy a HALF MILLION ROWS in 840 milliseconds - but takes 431 ms to copy just 10 rows.
Why is Snowflake so slow compared (for example) to Oracle 11g on premises:
Well - here's what Snowflake has do complete:
Compile the query and produce an efficient execution plan (plans are not currently cached as they often lead to a sub-optimal plan being executed on data which has significantly increased in volume)
Resume a virtual warehouse (if suspended)
Execute the query and write results to cloud storage
Synchronously replicate the data to two other data centres (typically a few miles apart)
Return OK to the user
Oracle on the other hands needs to:
Compile the query (if the query plan is not already cached)
Execute the query
Write results to local disk
If you REALLY want sub-second query performance on SELECT, INSERT, UPDATE and DELETE on Snowflake - it's coming soon. Just check out Snowflake Unistore and Hybrid Tables Explained
Hope this helps.
We are experiencing seemingly random timeouts on a two app (one ASP.Net and one WinForms) SQL Server application. I had SQL Profiler run during an hour block to see what might be causing the problem. I then isolated the times when the timeouts were occurring.
There are a large number of Reads but there is no large difference in the reads when the timeout errors occur and when they don't. There are virtually no writes during this period (primarily because everyone is getting time outs and can't write).
Example:
Timeout occurs 11:37. There are an average of 1500 transactions a minute leading up to the timeout, with about 5709219 reads.
That seems high EXCEPT that during a period in between timeouts (over a ten minute span), there are just as many transactions per minute and the reads are just as high. The reads do spike a little before the timeout (jumping up to over 6005708) but during the non-timeout period, they go as high as 8251468. The timeouts are occurring in both applications.
The bigger problem here is that this only started occurring in the past week and the application has been up and running for several years. So yes, the Profiler has given us a lot of data to work with but the current issue is the timeouts.
Is there something else that I should be possibly looking for in the Profiler or should I move to Performance Monitor (or another tool) over on the server?
One possible culprit might be the Database Size. The database is fairly large (>200 GB) but the AutoGrow setting was set to 1MB. Could it be that SQL Server is resizing itself and that transaction doesn't show itself in the profiler?
Many thanks
Thanks to the assistance here, I was able to identify a few bottlenecks but I wanted to outline my process to possibly help anyone going through this.
The #1 problem was found to be a high number of LOCK_MK_S entries found from the SQLDiag and other tools.
Run the Trace Profiler over two different periods of time. Comparing durations for similar methods led me to find that certain UPDATE calls were always taking the same amount of time, over 10 seconds.
Further investigation found that these UPDATE stored procs were updating a table with a trigger that was taking too much time. Since a trigger may lock the table while it completes, it was affecting every other query. ( See the comment section - I was incorrectly stating that the trigger would always lock the table - in our case, the trigger was preventing the lock from being released)
Watch the use of Triggers for doing major updates.
Sorry for the long introduction but before I can ask my question, I think giving the background would help understanding our problem much better.
We are using sql server 2008 for our web services as the backend and from time to time it takes too much time for responding back for the requests that supposed to run really fast, like taking more than 20 seconds for a select request that queries a table that has only 22 rows. We went through many potential areas that could cause the issue from indexes to stored procedures, triggers etc, and tried to optimize whatever we can like removing indexes that are not read but write frequently or adding NOLOCK for our select queries to reduce the locking of the tables (we are OK with dirty reads).
We also had our DBA's reviewed the server and benchmarked the components to see any bottlenecks in CPU, memory or disk subsystem, and found out that hardware-wise we are OK as well. And since the pikes are occurring occasionally, it is really hard to reproduce the error on production or development because most of the time when we rerun the same query it yields response times that we are expecting, which are short, not the one that has been experienced earlier.
Having said that, I almost have been suspicious about I/O although it is not seem to be a bottleneck. But I think I was just be able to reproduce the error after running an index fragmentation report for a specific table on the server, which immediately caused pikes in requests not only run against that table but also in other requests that query other tables. And since the DB, and the server, is shared with other applications we use and also from time to time queries can be run on the server and database that take long time is a common scenario for us, my suspicion regarding occasional I/O bottleneck is, I believe, becoming a fact.
Therefore I want to find out a way that would prioritize requests that are coming from web services which will be processed even if there are other resource sensitive queries being run. I have been looking for some kind of prioritization I described above since very beginning of the resolution process and found out that SQL Server 2008 has a feature called 'Resource Governor' that allows prioritization of the requests.
However, since I am not an expert on Resource Governor nor a DBA, I would like to ask other people's experience who may have used or is using Resource Governor, as well as whether I can prioritize I/O for a specific login or a specific stored procedure (For example, if one I/O intensive process is being run at the time we receive a web service request, can SQL server stops, or slows down, I/O activity for that process and give a priority to the request we just received?).
Thank you for anyone that spends time on reading or helping out in advance.
Some Hardware Details:
CPU: 2x Quad Core AMD Opteron 8354
Memory: 64GB
Disk Subsystem: Compaq EVA8100 series (I am not sure but it should be RAID 0+1 accross 8 HP HSV210 SCSI drives)
PS:And I can almost 100 percent sure that application servers are not causing the error and there is no bottleneck we can identify there.
Update 1:
I'll try to answer as much as I can for the following questions that gbn asked below. Please let me know if you are looking something else.
1) What kind of index and statistics maintenance do you have please?
We have a weekly running job that defrags indexes every Friday. In addition to that, Auto Create Statistics and Auto Update Statistics are enabled. And the spikes are occurring in other times than the fragmentation job as well.
2) What kind of write data volumes do you have?
Hard to answer.In addition to our web services, there is a front end application that accesses the same database and periodically resource intensive queries needs to be run to my knowledge, however, I don't know how to get, let's say weekly or daily, write amount to DB.
3) Have you profiled Recompilation and statistics update events?
Sorry for not be able to figure out this one. I didn't understand what you are asking about by this question. Can you provide more information for this question, if possible?
first thought is that statistics are being updated because of the data change threshold is reached causing execution plans to be rebuilt.
What kind of index and statistics maintenance do you have please? Note: index maintenance updates index stats, not column stats: you may need separate stats updates.
What kind of write data volumes do you have?
Have you profiled Recompilation and statistics update events?
In response to question 3) of your Update to the original question, take a look at the following reference on SQL Server Pedia. It provides an explanation of what query recompiles are and also goes on to explain how you can monitor for these events. What I believe gbn is asking (feel free to correct me sir :-) ) is are you seeing recompile events prior to the slow execution of the troublesome query. You can look for this occurring by using the SQL Server Profiler.
Reasons for Recompiling a Query Execution Plan