Setup :
3 member embedded cluster deployed as a spring boot jar.
Total keys on each member: 900K
Get operation is being attempted via a rest api.
Background:
I am trying to benchmark the replicated map of hazelcast.
Management center UI shows around 10k/s request being executed but avg get latency per sec is coming 0ms.
I believe it is not showing because it might be in microseconds.
Please let me know how to configure management center UI to show latency in micro/nanoseconds?
Management center UI shows around 10k/s request being executed but avg get latency per sec is coming 0ms.
I believe you're talking about Replicated Map Throughput Statistics in the replicated map details page. The Avg Get Latency column in that table shows on average how much time it took for a cluster member to execute the get operations for the time period that is selected on the top right corner of the table. For example, if you select Last Minute there, you only see the average time it took for the get operations in the last minute.
I believe it is not showing because it might be in microseconds.
Cluster is sending it as milliseconds (calculating it as nanoseconds in a newer cluster version but still sending as milliseconds). However, since a replicated map replicates all data on all members and every member contains the whole data set, get latency is typically very low as there's no network trip.
I guess that the way we render very small metric values confused you. In Management Center UI, we only show two fractional digits. You can see it in action in the below screenshots:
As you can see, since the value is very low, it is shown as 0. I believe we can do a better job rendering these values though (using a smaller time unit for example). I will create an issue for this on our private issue tracker.
Related
I have set up Hazelcast with JCache, where I want to get a more comprehensive statistic of my cache usage. Mainly I want to see the cache entries' stats over some period of time (e.g. past 24 hrs), but the graph seems only allows me to select a point of time, and show the 5 mins statistics of it.
Is there a way I can update this behavior and create a more meaningful graph? Thanks!
Hazelcast MC version: 5.0.2
Example:
I want this graph to allow me to select a period of time, instead of a point of time.
We plan to implement a Spark Structured Streaming application which will consume a continuous flow of data: evolution of a metric value over time.
This streaming application will work with a window size of 7 days (and a sliding window) in order to frequently calculate the average of the metric value over the last 7 days.
1- Will Spark retain all those 7 days of data (impacting a lot the memory consumed), OR Spark continuously calculates and updates the average requested (and then get rid of handled data) and so does not impact so much memory consumed (not retaining 7 days of data) ?
2- In case answer to first question is that those 7 days of data are retained, does the usage of watermark prevent this retention ?
Let’s say that we have a watermark of 1 hour; will only 1 hour of data be retained in Spark, OR 7 days are still retained in spark memory and watermark is here just for ignoring new data coming in with a datatimestamp older than 1 hour ?
Window Size 7 is definitely a significant one, but it also depends on the streaming data volume/records coming in. The trick lies in how to use the Window duration, update interval, output mode and if necessary the watermark (if the business rule is not impacted)
1- If the streaming is configured to be of tumbling window size (ie the window duration is same as the update duration), with complete mode, you may end up full data being kept in memory for 7 days. However, if you configure the window duration to be 7 days with an update of every x minutes, aggregates will be calculated every x minutes and only the result data will be kept in memory. Hence look at the window API parameters and configure the way to get the results.
2- Watermark brings a different behaviour and it ignores the records before the watermark duration and update the result tables after every micro batch crosses the water mark time. If your business rule is ok to include watermark calculation, it is fine to use it too.
It is good to go through the API in detail, output modes and watermark usage at enter link description here
This would help to choose the right combination.
I just launched an Azure SQL Database, and the DTU and CPU usage is behaving strangely. The database is only receiving about 30 requests per minute, and the CPU/DTU will be extremely low for hours, and then jump up to 100% and stay there (with no increase in the number of requests that triggers this). When I click to view the top queries, none of them are above 1% cpu usage. I started out on a 5 DTU plan, and yesterday upgraded to 20 DTUs and the same behavior is occurring. Any idea what else might cause the DTU/CPU to get maxed out? See images below:
https://i.imgur.com/LdbYTPw.png
https://i.imgur.com/jlus3FM.png
Thanks in advance for any advice!
Joe
EDIT: I'm getting closer, I found these repeated entries in the error log. (about 8 - 10 per SECOND)
"The incoming request has too many parameters. The server supports a maximum of 2100 parameters. Reduce the number of parameters and resend the request."
The thing is, the App Service that queries the database is only doing simple selects, updates, and inserts... none of which uses any complex WHERE IN statement. Furthermore, every query is wrapped in a try/catch block, and I'm never seeing an exception like this.
Where could these large queries be originating from?
You are only seeing the CPU component of the DTU graph, what about the "Data IO" and the "Log IO" components? Look at the top 5 queries on the 3 sections, and let me know if you find a query that start with "SELECT Statman ...". If you see that, then the Auto Update Statistics process is creating those DTU spikes.
I would suggest to install the sp_whoisactive script so that you can see what's going on more easily:
http://whoisactive.com/
I have data in the format { host | metric | value | time-stamp }. We have hosts all around the world reporting metrics.
I'm a little confused about using window operations (say, 1 hour) to process data like this.
Can I tell my window when to start, or does it just start when the application starts? I want to ensure I'm aggregating all data from hour 11 of the day, for example. If my window starts at 10:50, I'll just get 10:50-11:50 and miss 10 minutes.
Even if the window is perfect, data may arrive late.
How do people handle this kind of issue? Do they make windows far bigger than needed and just grab the data they care about on every batch cycle (kind of sliding)?
In the past, I worked on a large-scale IoT platform and solved that problem by considering that the windows were only partial calculations. I modeled the backend (Cassandra) to receive more than 1 record for each window. The actual value of any given window would be the addition of all -potentially partial- records found for that window.
So, a perfect window would be 1 record, a split window would be 2 records, late-arrivals are naturally supported but only accepted up to a certain 'age' threshold. Reconciliation was done at read time. As this platform was orders of magnitude heavier in terms of writes vs reads, it made for a good compromise.
After speaking with people in depth on MapR forums, the consensus seems to be that hourly and daily aggregations should not be done in a stream, but rather in a separate batch job once the data is ready.
When doing streaming you should stick to small batches with windows that are relatively small multiples of the streaming interval. Sliding windows can be useful for, say, trends over the last 50 batches. Using them for tasks as large as an hour or a day doesn't seem sensible though.
Also, I don't believe you can tell your batches when to start/stop, etc.
The facts:
1 Azure SQL S0 instance
a few tables one of them containing ~ 8.6 Million Rows and 1 PK
Running a Count-query on this table takes nearly 30 minutes (!) to complete.
Upscaling the instance from S0 to S1 reduces the query time to 13 minutes:
Looking into Azure Portal (new version) the resource-usage-monitor shows the following:
Questions:
Does anyone else consider even 13 minutes as rediculos for a simple COUNT()?
Does the second screenshot meen that during the 100%-period my instance isn't responding to other requests?
Why are my metrics limited to 100% in both S0 and S1? (see look under "Which Service Tier is Right for My Database?" stating " These values can be above 100% (a big improvement over the values in the preview that were limited to a maximum of 100).") I'd expect the S0 to bee like on 150% or so if the quoted statement is true.
I'm interested in experiences regarding usage of databases with more than 1.000 records or so from other people. I don't see how a S*-scaled Azure SQL for 22 - 55 € per month could help me in upscaling-strategies at the moment.
Azure SQL Database editions provide increasing levels of DTUs from Basic -> Standard -> Premium levels (CPU,IO,Memory and other resources - see https://msdn.microsoft.com/en-us/library/azure/dn741336.aspx). Once your query reaches its limits of DTU (100%) in any of these resource dimensions, it will continue to receive these resources at that level (but not more) and that may increase the latency in completing the request. It looks like in your scenario above, the query is hitting its DTU limit (10 DTUs for S0 and 20 for S1). You can see the individual resource usage percentages (CPU, Data IO or Log IO) by adding these metrics to the same graph, or by querying the DMV sys.dm_db_resource_stats.
Here is a blog that provides more information on appropriately sizing your database performance levels. http://azure.microsoft.com/blog/2014/09/11/azure-sql-database-introduces-new-near-real-time-performance-metrics/
To your specific questions
1) As you have 8.6 million rows, database needs to scan the index entries to get the count back. So, it may be hitting the IO limit for the edition here.
2) If you have multiple concurrent queries running against your DB, they will be scheduled appropriately to not starve one request or the other. But latencies may increase further for all queries since you will be hitting the available resource limits.
3) For older Web/Business editions, you may be able to see the metric values going beyond 100% (they are normalized to the limits of an S2 level), as they don't have any specific limits and run in a resource-shared environment with other customer loads. For the new editions, metrics will never exceed 100%, because system guarantees you resources upto 100% of that edition's limits, but no more. This provides predictable, guaranteed amount of resources for your DB unlike Web/Business editions, where you may get very little or lot more resources at different times depending on other competing customer DB workloads running on the same machine.
Hope this helps.
-- Srini