Re: scaling a low latency service with HBase
Here are a few of my thoughts: If possible, you might want to localize your data to a few regions if you can and then may be have exclusive access to those regions. This way, external load will not impact you. I have heard that write penalty of SSDs is quite high. But I think, they will still be better than spinning disks. Also( I read a while back), with SSDs you get a quota of max possible writes so if you are write heavy, it may be an issue. If the data lives only on a few regions, then it is only on a few servers which means it won't fit in RAM, so it comes back to SSD's. I have also read that there is a limited number of lifetime writes on SSDs, and I'm very interested in how that interacts with HBase's write pipeline (which was designed for spinning disks). I would imagine that each HLog sync would cause an SSD write at each DataNode in the pipeline. In that case I would expect wear leveling to give sufficient life. I would presume any solution like cache which is built within Hbase will suffer from the same issues you described. OTOH, External caching can help but then you need to invest there and maintain cache to source consistency - might be another issue. If you are just doing KV lookups and no ranges, why don't just use KV stores like Cassandra or may be explore other Nosql solns like Mongo etc? Are there any that would have particular advantages when it came to SSDs? If your data lookups exhibits temporal locality, external, client side cache pools may help. My 2c, Abhishek -Original Message- From: ddlat...@gmail.com [mailto:ddlat...@gmail.com] On Behalf Of Dave Latham Sent: Friday, October 19, 2012 4:31 PM To: user@hbase.apache.org Subject: scaling a low latency service with HBase I need to scale an internal service / datastore that is currently hosted on an HBase cluster and wanted to ask for advice from anyone out there who may have some to share. The service does simple key value lookups on 20 byte keys to 20-40 byte values. It currently has about 5 billion entries (200GB), and processes about 40k random reads per second, and about 2k random writes per second. It currently delivers a median response at 2ms, 90% at 20ms, 99% at 200ms, 99.5% at 5000ms - but the mean is 58ms which is no longer meeting our needs very well. It is persistent and highly available. I need to measure its working set more closely, but I believe that around 20-30% (randomly distributed) of the data is accessed each day. I want a system that can scale to at least 10x current levels (50 billion entries - 2TB, 400k requests per second) and achieve a mean 5ms (ideally 1-2ms) and 99.5% 50ms response time for reads while maintaining persistence and reasonably high availability (99.9%). Writes would ideally be in the same as range but we could probably tolerate a mean more in the 20-30ms range. Clearly for that latency, spinning disks won't cut it. The current service is running out of an hbase cluster that is shared with many other things and when those other things hit the disk and network hard is when it degrades. The cluster has hundreds of nodes and this data is fits in a small slice of block cache across most of them. The concerns are that its performance is impacted by other loads and that as it continues to grow there may not be enough space in the current cluster's shared block cache. So I'm looking for something that will serve out of memory (backed by disk for persistence) or from SSDs. A few questions that I would love to hear answers for: - Does HBase sound like a good match as this grows? - Does anyone have experience running HBase over SSDs? What sort of latency and requests per second have you been able to achieve? - Is anyone using a row cache on top of (or built into) HBase? I think there's been a bit of discussion on occasion but it hasn't gone very far. There would be some overhead for each row. It seems that if we were to continue to rely on memory + disks this could reduce the memory required. - Does anyone have alternate suggestions for such a service? Dave
Re: scaling a low latency service with HBase
On Fri, Oct 19, 2012 at 5:22 PM, Amandeep Khurana ama...@gmail.com wrote: Answers inline On Fri, Oct 19, 2012 at 4:31 PM, Dave Latham lat...@davelink.net wrote: I need to scale an internal service / datastore that is currently hosted on an HBase cluster and wanted to ask for advice from anyone out there who may have some to share. The service does simple key value lookups on 20 byte keys to 20-40 byte values. It currently has about 5 billion entries (200GB), and processes about 40k random reads per second, and about 2k random writes per second. It currently delivers a median response at 2ms, 90% at 20ms, 99% at 200ms, 99.5% at 5000ms - but the mean is 58ms which is no longer meeting our needs very well. It is persistent and highly available. I need to measure its working set more closely, but I believe that around 20-30% (randomly distributed) of the data is accessed each day. I want a system that can scale to at least 10x current levels (50 billion entries - 2TB, 400k requests per second) and achieve a mean 5ms (ideally 1-2ms) and 99.5% 50ms response time for reads while maintaining persistence and reasonably high availability (99.9%). Writes would ideally be in the same as range but we could probably tolerate a mean more in the 20-30ms range. Clearly for that latency, spinning disks won't cut it. The current service is running out of an hbase cluster that is shared with many other things and when those other things hit the disk and network hard is when it degrades. The cluster has hundreds of nodes and this data is fits in a small slice of block cache across most of them. The concerns are that its performance is impacted by other loads and that as it continues to grow there may not be enough space in the current cluster's shared block cache. So I'm looking for something that will serve out of memory (backed by disk for persistence) or from SSDs. A few questions that I would love to hear answers for: - Does HBase sound like a good match as this grows? Yes. The key to get more predictable performance is to separate out workloads. What are the other things that are using the same physical hardware and impacting performance? Have you measure performance when nothing else is running on the cluster? There are several other things sharing the cluster and using it more heavily than this service - both online request handling as well as some large batch map reduce jobs. When the large jobs aren't running the performance is acceptable and typically in the 1-2ms mean reads range. (Served out of block cache). - Does anyone have experience running HBase over SSDs? What sort of latency and requests per second have you been able to achieve? I don't believe many people are actually running this in production yet. Some folks have done some research on this topic and posted blogs (eg: http://hadoopblog.blogspot.com/2012/05/hadoop-and-solid-state-drives.html) but there's not a whole lot more than that to go by at this point. Thanks, that's a really helpful reference. It sounds like it could be a big gain over disks already but that the bottleneck would move from IO to CPU and that there would be significant work to be done. - Is anyone using a row cache on top of (or built into) HBase? I think there's been a bit of discussion on occasion but it hasn't gone very far. There would be some overhead for each row. It seems that if we were to continue to rely on memory + disks this could reduce the memory required. - Does anyone have alternate suggestions for such a service? The biggest recommendation is to separate out the workloads and then start planning for more hardware or additional components to get better performance. Right, that's why I'm looking to separate this service out. However, I'd like to go with a much smaller set of nodes for this particular service rather than duplicating the large, expensive cluster.
RE: scaling a low latency service with HBase
Here are a few of my thoughts: If possible, you might want to localize your data to a few regions if you can and then may be have exclusive access to those regions. This way, external load will not impact you. I have heard that write penalty of SSDs is quite high. But I think, they will still be better than spinning disks. Also( I read a while back), with SSDs you get a quota of max possible writes so if you are write heavy, it may be an issue. I would presume any solution like cache which is built within Hbase will suffer from the same issues you described. OTOH, External caching can help but then you need to invest there and maintain cache to source consistency - might be another issue. If you are just doing KV lookups and no ranges, why don't just use KV stores like Cassandra or may be explore other Nosql solns like Mongo etc? If your data lookups exhibits temporal locality, external, client side cache pools may help. My 2c, Abhishek -Original Message- From: ddlat...@gmail.com [mailto:ddlat...@gmail.com] On Behalf Of Dave Latham Sent: Friday, October 19, 2012 4:31 PM To: user@hbase.apache.org Subject: scaling a low latency service with HBase I need to scale an internal service / datastore that is currently hosted on an HBase cluster and wanted to ask for advice from anyone out there who may have some to share. The service does simple key value lookups on 20 byte keys to 20-40 byte values. It currently has about 5 billion entries (200GB), and processes about 40k random reads per second, and about 2k random writes per second. It currently delivers a median response at 2ms, 90% at 20ms, 99% at 200ms, 99.5% at 5000ms - but the mean is 58ms which is no longer meeting our needs very well. It is persistent and highly available. I need to measure its working set more closely, but I believe that around 20-30% (randomly distributed) of the data is accessed each day. I want a system that can scale to at least 10x current levels (50 billion entries - 2TB, 400k requests per second) and achieve a mean 5ms (ideally 1-2ms) and 99.5% 50ms response time for reads while maintaining persistence and reasonably high availability (99.9%). Writes would ideally be in the same as range but we could probably tolerate a mean more in the 20-30ms range. Clearly for that latency, spinning disks won't cut it. The current service is running out of an hbase cluster that is shared with many other things and when those other things hit the disk and network hard is when it degrades. The cluster has hundreds of nodes and this data is fits in a small slice of block cache across most of them. The concerns are that its performance is impacted by other loads and that as it continues to grow there may not be enough space in the current cluster's shared block cache. So I'm looking for something that will serve out of memory (backed by disk for persistence) or from SSDs. A few questions that I would love to hear answers for: - Does HBase sound like a good match as this grows? - Does anyone have experience running HBase over SSDs? What sort of latency and requests per second have you been able to achieve? - Is anyone using a row cache on top of (or built into) HBase? I think there's been a bit of discussion on occasion but it hasn't gone very far. There would be some overhead for each row. It seems that if we were to continue to rely on memory + disks this could reduce the memory required. - Does anyone have alternate suggestions for such a service? Dave
Re: scaling a low latency service with HBase
Answers inline On Fri, Oct 19, 2012 at 4:31 PM, Dave Latham lat...@davelink.net wrote: I need to scale an internal service / datastore that is currently hosted on an HBase cluster and wanted to ask for advice from anyone out there who may have some to share. The service does simple key value lookups on 20 byte keys to 20-40 byte values. It currently has about 5 billion entries (200GB), and processes about 40k random reads per second, and about 2k random writes per second. It currently delivers a median response at 2ms, 90% at 20ms, 99% at 200ms, 99.5% at 5000ms - but the mean is 58ms which is no longer meeting our needs very well. It is persistent and highly available. I need to measure its working set more closely, but I believe that around 20-30% (randomly distributed) of the data is accessed each day. I want a system that can scale to at least 10x current levels (50 billion entries - 2TB, 400k requests per second) and achieve a mean 5ms (ideally 1-2ms) and 99.5% 50ms response time for reads while maintaining persistence and reasonably high availability (99.9%). Writes would ideally be in the same as range but we could probably tolerate a mean more in the 20-30ms range. Clearly for that latency, spinning disks won't cut it. The current service is running out of an hbase cluster that is shared with many other things and when those other things hit the disk and network hard is when it degrades. The cluster has hundreds of nodes and this data is fits in a small slice of block cache across most of them. The concerns are that its performance is impacted by other loads and that as it continues to grow there may not be enough space in the current cluster's shared block cache. So I'm looking for something that will serve out of memory (backed by disk for persistence) or from SSDs. A few questions that I would love to hear answers for: - Does HBase sound like a good match as this grows? Yes. The key to get more predictable performance is to separate out workloads. What are the other things that are using the same physical hardware and impacting performance? Have you measure performance when nothing else is running on the cluster? - Does anyone have experience running HBase over SSDs? What sort of latency and requests per second have you been able to achieve? I don't believe many people are actually running this in production yet. Some folks have done some research on this topic and posted blogs (eg: http://hadoopblog.blogspot.com/2012/05/hadoop-and-solid-state-drives.html) but there's not a whole lot more than that to go by at this point. - Is anyone using a row cache on top of (or built into) HBase? I think there's been a bit of discussion on occasion but it hasn't gone very far. There would be some overhead for each row. It seems that if we were to continue to rely on memory + disks this could reduce the memory required. - Does anyone have alternate suggestions for such a service? The biggest recommendation is to separate out the workloads and then start planning for more hardware or additional components to get better performance. Dave
Re: scaling a low latency service with HBase
What Amandeep said, and also: You said your working set is randomly distributed but, if frequent invalidation isn't a concern and read accesses are still clustered temporally, an in-memory cache out in front of the cluster would smooth over periods when the disks are busy servicing MR workload or whatever else is going on. Another way of separating workloads. Regarding use of SSDs with HBase, this is an area I intend to get direct experience with soon, and will report back findings as they become available. On Fri, Oct 19, 2012 at 5:22 PM, Amandeep Khurana ama...@gmail.com wrote: Answers inline On Fri, Oct 19, 2012 at 4:31 PM, Dave Latham lat...@davelink.net wrote: I need to scale an internal service / datastore that is currently hosted on an HBase cluster and wanted to ask for advice from anyone out there who may have some to share. The service does simple key value lookups on 20 byte keys to 20-40 byte values. It currently has about 5 billion entries (200GB), and processes about 40k random reads per second, and about 2k random writes per second. It currently delivers a median response at 2ms, 90% at 20ms, 99% at 200ms, 99.5% at 5000ms - but the mean is 58ms which is no longer meeting our needs very well. It is persistent and highly available. I need to measure its working set more closely, but I believe that around 20-30% (randomly distributed) of the data is accessed each day. I want a system that can scale to at least 10x current levels (50 billion entries - 2TB, 400k requests per second) and achieve a mean 5ms (ideally 1-2ms) and 99.5% 50ms response time for reads while maintaining persistence and reasonably high availability (99.9%). Writes would ideally be in the same as range but we could probably tolerate a mean more in the 20-30ms range. Clearly for that latency, spinning disks won't cut it. The current service is running out of an hbase cluster that is shared with many other things and when those other things hit the disk and network hard is when it degrades. The cluster has hundreds of nodes and this data is fits in a small slice of block cache across most of them. The concerns are that its performance is impacted by other loads and that as it continues to grow there may not be enough space in the current cluster's shared block cache. So I'm looking for something that will serve out of memory (backed by disk for persistence) or from SSDs. A few questions that I would love to hear answers for: - Does HBase sound like a good match as this grows? Yes. The key to get more predictable performance is to separate out workloads. What are the other things that are using the same physical hardware and impacting performance? Have you measure performance when nothing else is running on the cluster? - Does anyone have experience running HBase over SSDs? What sort of latency and requests per second have you been able to achieve? I don't believe many people are actually running this in production yet. Some folks have done some research on this topic and posted blogs (eg: http://hadoopblog.blogspot.com/2012/05/hadoop-and-solid-state-drives.html) but there's not a whole lot more than that to go by at this point. - Is anyone using a row cache on top of (or built into) HBase? I think there's been a bit of discussion on occasion but it hasn't gone very far. There would be some overhead for each row. It seems that if we were to continue to rely on memory + disks this could reduce the memory required. - Does anyone have alternate suggestions for such a service? The biggest recommendation is to separate out the workloads and then start planning for more hardware or additional components to get better performance. Dave -- Best regards, - Andy Problems worthy of attack prove their worth by hitting back. - Piet Hein (via Tom White)
