Re: SSD vs. HDD
If you're experiencing high I/O load and not getting any Java OutOfMemory (OOM) errors, you should try to keep your heap size as low as possible as this provides the OS filesystem cache with more memory, which will reduce read I/O load significantly. I'm not familiar the performance of Windows filesystems, but I imagine NTFS is somewhat on a par with what we're familiar with in Linux. The row cache will be useful in cases where you have a high read/write ratio (more reads than writes) especially if most of those reads are confined to a specific subset of data. The key cache will also improve read performance (which will be your main I/O bottleneck) with much less of a memory impact, so in your case I would recommend enabling it for as many keys as possible. Riptano have a pretty decent explanation of tuning Cassandra that I highly recommend you read: http://www.riptano.com/docs/0.6.5/operations/tuning http://www.riptano.com/docs/0.6.5/operations/tuningRegards, Nick Telford On 4 November 2010 22:20, Alaa Zubaidi alaa.zuba...@pdf.com wrote: Thanks for the advise... We are running on Windows, and I just added more memory to my system, 16G I will run the test again with 8G heap. The load is continues, however, the CPU usage is around 40% with max of 70%. As for cache, I am not using cache, because I am under the impression that cache in my case, where the data keeps changing very quickly in and out of cache, is not a good idea? Thanks On 11/4/2010 3:14 AM, Nick Telford wrote: If you're bottle-necking on read I/O making proper use of Cassandras key cache and row cache will improve things dramatically. A little maths using the numbers you've provided tells me that you have about 80GB of hot data (data valid in a 4 hour period). That's obviously too much to directly cache, but you can probably cache some or all of the row keys, depending on your column distribution among keys. This will prevent reads from having to hit the indexes for the relevant sstables - eliminating a seek per sstable. If you have a subset of this data that is read more than the rest, the row cache will help you out a lot too. Have a look at your access patterns and see if it's worthwhile caching some rows. If you make progress using the various caches, but don't have enough memory, I'd explore the costs of expanding the available memory compared to switching to SSDs as I imagine it'd be cheaper and would last longer. Finally, given your particular deletion pattern, it's probably worth looking at 0.7 and upgrading once it is released as stable. CASSANDRA-699[1] adds support for TTL columns that automatically expire and get removed (during compaction) without the need for a manual deletion mechanism. Failing this, since data older than 4 hours is no longer relevant, you should reduce your GCGraceSeconds= 4 hours. This will ensure deleted data is removed faster, keeping your sstables smaller and allowing the fs cache to operate more effectively. 1: https://issues.apache.org/jira/browse/CASSANDRA-699 On 4 November 2010 08:18, Peter Schullerpeter.schul...@infidyne.com wrote: I am having time out errors while reading. I have 5 CFs but two CFs with high write/read. The data is organized in time series rows, in CF1 the new rows are read every 10 seconds and then the whole rows are deleted, While in CF2 the rows are read in different time range slices and eventually deleted may be after few hours. So the first thing to do is to confirm what the bottleneck is. If you're having timeouts on reads, and assuming your not doing reads of hot-in-cache data so fast that CPU is the bottleneck (and given that you ask about SSD), the hypothesis then is that you're disk bound due to seeking. Observe the node(s) and in particular use iostat -x -k 1 (or an equivalent graph) and look at the %util and %avgqu-sz columns to confirm that you are indeed disk-bound. Unless you're doing large reads, you will likely see, on average, small reads in amounts that simply saturate underlying storage, %util at 100% and the avgu-sz will probably be approaching the level of concurrency of your read traffic. Now, assuming that is true, the question is why. So: (1) Are you continually saturating disk or just periodically? (2) If periodically, does the periods of saturation correlate with compaction being done by Cassandra (or for that matter something else)? (3) What is your data set size relative to system memory? What is your system memory and JVM heap size? (Relevant because it is important to look at how much memory the kernel will use for page caching.) As others have mentioned, the amount of reads done on disk for each read form the database (assuming data is not in cache) can be affected by how data is written (e.g., partial row writes etc). That is one thing that can be addressed, as is re-structuring data to allow reading more sequentially (if possible). That only helps along
Re: SSD vs. HDD
I am having time out errors while reading. I have 5 CFs but two CFs with high write/read. The data is organized in time series rows, in CF1 the new rows are read every 10 seconds and then the whole rows are deleted, While in CF2 the rows are read in different time range slices and eventually deleted may be after few hours. So the first thing to do is to confirm what the bottleneck is. If you're having timeouts on reads, and assuming your not doing reads of hot-in-cache data so fast that CPU is the bottleneck (and given that you ask about SSD), the hypothesis then is that you're disk bound due to seeking. Observe the node(s) and in particular use iostat -x -k 1 (or an equivalent graph) and look at the %util and %avgqu-sz columns to confirm that you are indeed disk-bound. Unless you're doing large reads, you will likely see, on average, small reads in amounts that simply saturate underlying storage, %util at 100% and the avgu-sz will probably be approaching the level of concurrency of your read traffic. Now, assuming that is true, the question is why. So: (1) Are you continually saturating disk or just periodically? (2) If periodically, does the periods of saturation correlate with compaction being done by Cassandra (or for that matter something else)? (3) What is your data set size relative to system memory? What is your system memory and JVM heap size? (Relevant because it is important to look at how much memory the kernel will use for page caching.) As others have mentioned, the amount of reads done on disk for each read form the database (assuming data is not in cache) can be affected by how data is written (e.g., partial row writes etc). That is one thing that can be addressed, as is re-structuring data to allow reading more sequentially (if possible). That only helps along one dimension though - lessening, somewhat, the cost of cold reads. The gains may be limited and the real problem may be that you simply need more memory for caching and/or more IOPS from your storage (i.e., more disks, maybe SSD, etc). If on the other hand you're normally completely fine and you're just seeing periods of saturation associated with compaction, this may be mitigated by software improvements by possibly rate limiting reads and/or writes during compaction and avoiding buffer cache thrashing. There's a JIRA ticket for direct I/O (https://issues.apache.org/jira/browse/CASSANDRA-1470). I don't think there's a JIRA ticket for rate limiting, but I suspect, since you're doing time series data, that you're not storing very large values - and I would expect compaction to be CPU bound rather than being close to saturate disk. In either case, please do report back as it's interesting to figure out what kind of performance issues people are seeing. -- / Peter Schuller
Re: SSD vs. HDD
If you're bottle-necking on read I/O making proper use of Cassandras key cache and row cache will improve things dramatically. A little maths using the numbers you've provided tells me that you have about 80GB of hot data (data valid in a 4 hour period). That's obviously too much to directly cache, but you can probably cache some or all of the row keys, depending on your column distribution among keys. This will prevent reads from having to hit the indexes for the relevant sstables - eliminating a seek per sstable. If you have a subset of this data that is read more than the rest, the row cache will help you out a lot too. Have a look at your access patterns and see if it's worthwhile caching some rows. If you make progress using the various caches, but don't have enough memory, I'd explore the costs of expanding the available memory compared to switching to SSDs as I imagine it'd be cheaper and would last longer. Finally, given your particular deletion pattern, it's probably worth looking at 0.7 and upgrading once it is released as stable. CASSANDRA-699[1] adds support for TTL columns that automatically expire and get removed (during compaction) without the need for a manual deletion mechanism. Failing this, since data older than 4 hours is no longer relevant, you should reduce your GCGraceSeconds = 4 hours. This will ensure deleted data is removed faster, keeping your sstables smaller and allowing the fs cache to operate more effectively. 1: https://issues.apache.org/jira/browse/CASSANDRA-699 On 4 November 2010 08:18, Peter Schuller peter.schul...@infidyne.comwrote: I am having time out errors while reading. I have 5 CFs but two CFs with high write/read. The data is organized in time series rows, in CF1 the new rows are read every 10 seconds and then the whole rows are deleted, While in CF2 the rows are read in different time range slices and eventually deleted may be after few hours. So the first thing to do is to confirm what the bottleneck is. If you're having timeouts on reads, and assuming your not doing reads of hot-in-cache data so fast that CPU is the bottleneck (and given that you ask about SSD), the hypothesis then is that you're disk bound due to seeking. Observe the node(s) and in particular use iostat -x -k 1 (or an equivalent graph) and look at the %util and %avgqu-sz columns to confirm that you are indeed disk-bound. Unless you're doing large reads, you will likely see, on average, small reads in amounts that simply saturate underlying storage, %util at 100% and the avgu-sz will probably be approaching the level of concurrency of your read traffic. Now, assuming that is true, the question is why. So: (1) Are you continually saturating disk or just periodically? (2) If periodically, does the periods of saturation correlate with compaction being done by Cassandra (or for that matter something else)? (3) What is your data set size relative to system memory? What is your system memory and JVM heap size? (Relevant because it is important to look at how much memory the kernel will use for page caching.) As others have mentioned, the amount of reads done on disk for each read form the database (assuming data is not in cache) can be affected by how data is written (e.g., partial row writes etc). That is one thing that can be addressed, as is re-structuring data to allow reading more sequentially (if possible). That only helps along one dimension though - lessening, somewhat, the cost of cold reads. The gains may be limited and the real problem may be that you simply need more memory for caching and/or more IOPS from your storage (i.e., more disks, maybe SSD, etc). If on the other hand you're normally completely fine and you're just seeing periods of saturation associated with compaction, this may be mitigated by software improvements by possibly rate limiting reads and/or writes during compaction and avoiding buffer cache thrashing. There's a JIRA ticket for direct I/O (https://issues.apache.org/jira/browse/CASSANDRA-1470). I don't think there's a JIRA ticket for rate limiting, but I suspect, since you're doing time series data, that you're not storing very large values - and I would expect compaction to be CPU bound rather than being close to saturate disk. In either case, please do report back as it's interesting to figure out what kind of performance issues people are seeing. -- / Peter Schuller
Re: SSD vs. HDD
Its a little bit different than what most people use it for, and that's why we are trying to test it, to see if we can benefit from the speed of writing/reading, scalability when and if we need it, and also the coast. and part of the testing we are doing, is trying to see how many nodes do we need in our cluster, since we know the data volume, so far, its almost double what we were calculating and hoping, which is a not so good thing.. On 11/4/2010 4:18 AM, Juho Mäkinen wrote: Do you really need Cassandra to store just 80 GB data for just four hours? It might be just me, but this sounds like quite far fetched from normal Cassandra usage. Cassandra isn't happy unless you run enough nodes to cover one or two node doing compaction (which hurts the node performance). Are you ready to run at least two, preferably three C* nodes to store just 80GB of data? - Garo On Thu, Nov 4, 2010 at 12:14 PM, Nick Telfordnick.telf...@gmail.com wrote: If you're bottle-necking on read I/O making proper use of Cassandras key cache and row cache will improve things dramatically. A little maths using the numbers you've provided tells me that you have about 80GB of hot data (data valid in a 4 hour period). That's obviously too much to directly cache, but you can probably cache some or all of the row keys, depending on your column distribution among keys. This will prevent reads from having to hit the indexes for the relevant sstables - eliminating a seek per sstable. If you have a subset of this data that is read more than the rest, the row cache will help you out a lot too. Have a look at your access patterns and see if it's worthwhile caching some rows. If you make progress using the various caches, but don't have enough memory, I'd explore the costs of expanding the available memory compared to switching to SSDs as I imagine it'd be cheaper and would last longer. Finally, given your particular deletion pattern, it's probably worth looking at 0.7 and upgrading once it is released as stable. CASSANDRA-699[1] adds support for TTL columns that automatically expire and get removed (during compaction) without the need for a manual deletion mechanism. Failing this, since data older than 4 hours is no longer relevant, you should reduce your GCGraceSeconds= 4 hours. This will ensure deleted data is removed faster, keeping your sstables smaller and allowing the fs cache to operate more effectively. 1: https://issues.apache.org/jira/browse/CASSANDRA-699 On 4 November 2010 08:18, Peter Schullerpeter.schul...@infidyne.com wrote: I am having time out errors while reading. I have 5 CFs but two CFs with high write/read. The data is organized in time series rows, in CF1 the new rows are read every 10 seconds and then the whole rows are deleted, While in CF2 the rows are read in different time range slices and eventually deleted may be after few hours. So the first thing to do is to confirm what the bottleneck is. If you're having timeouts on reads, and assuming your not doing reads of hot-in-cache data so fast that CPU is the bottleneck (and given that you ask about SSD), the hypothesis then is that you're disk bound due to seeking. Observe the node(s) and in particular use iostat -x -k 1 (or an equivalent graph) and look at the %util and %avgqu-sz columns to confirm that you are indeed disk-bound. Unless you're doing large reads, you will likely see, on average, small reads in amounts that simply saturate underlying storage, %util at 100% and the avgu-sz will probably be approaching the level of concurrency of your read traffic. Now, assuming that is true, the question is why. So: (1) Are you continually saturating disk or just periodically? (2) If periodically, does the periods of saturation correlate with compaction being done by Cassandra (or for that matter something else)? (3) What is your data set size relative to system memory? What is your system memory and JVM heap size? (Relevant because it is important to look at how much memory the kernel will use for page caching.) As others have mentioned, the amount of reads done on disk for each read form the database (assuming data is not in cache) can be affected by how data is written (e.g., partial row writes etc). That is one thing that can be addressed, as is re-structuring data to allow reading more sequentially (if possible). That only helps along one dimension though - lessening, somewhat, the cost of cold reads. The gains may be limited and the real problem may be that you simply need more memory for caching and/or more IOPS from your storage (i.e., more disks, maybe SSD, etc). If on the other hand you're normally completely fine and you're just seeing periods of saturation associated with compaction, this may be mitigated by software improvements by possibly rate limiting reads and/or writes during compaction and avoiding buffer cache thrashing. There's a JIRA ticket for direct I/O (https://issues.apache.org/jira/browse/CASSANDRA-1470). I don't think there's a
Re: SSD vs. HDD
Thanks for the advise... We are running on Windows, and I just added more memory to my system, 16G I will run the test again with 8G heap. The load is continues, however, the CPU usage is around 40% with max of 70%. As for cache, I am not using cache, because I am under the impression that cache in my case, where the data keeps changing very quickly in and out of cache, is not a good idea? Thanks On 11/4/2010 3:14 AM, Nick Telford wrote: If you're bottle-necking on read I/O making proper use of Cassandras key cache and row cache will improve things dramatically. A little maths using the numbers you've provided tells me that you have about 80GB of hot data (data valid in a 4 hour period). That's obviously too much to directly cache, but you can probably cache some or all of the row keys, depending on your column distribution among keys. This will prevent reads from having to hit the indexes for the relevant sstables - eliminating a seek per sstable. If you have a subset of this data that is read more than the rest, the row cache will help you out a lot too. Have a look at your access patterns and see if it's worthwhile caching some rows. If you make progress using the various caches, but don't have enough memory, I'd explore the costs of expanding the available memory compared to switching to SSDs as I imagine it'd be cheaper and would last longer. Finally, given your particular deletion pattern, it's probably worth looking at 0.7 and upgrading once it is released as stable. CASSANDRA-699[1] adds support for TTL columns that automatically expire and get removed (during compaction) without the need for a manual deletion mechanism. Failing this, since data older than 4 hours is no longer relevant, you should reduce your GCGraceSeconds= 4 hours. This will ensure deleted data is removed faster, keeping your sstables smaller and allowing the fs cache to operate more effectively. 1: https://issues.apache.org/jira/browse/CASSANDRA-699 On 4 November 2010 08:18, Peter Schullerpeter.schul...@infidyne.comwrote: I am having time out errors while reading. I have 5 CFs but two CFs with high write/read. The data is organized in time series rows, in CF1 the new rows are read every 10 seconds and then the whole rows are deleted, While in CF2 the rows are read in different time range slices and eventually deleted may be after few hours. So the first thing to do is to confirm what the bottleneck is. If you're having timeouts on reads, and assuming your not doing reads of hot-in-cache data so fast that CPU is the bottleneck (and given that you ask about SSD), the hypothesis then is that you're disk bound due to seeking. Observe the node(s) and in particular use iostat -x -k 1 (or an equivalent graph) and look at the %util and %avgqu-sz columns to confirm that you are indeed disk-bound. Unless you're doing large reads, you will likely see, on average, small reads in amounts that simply saturate underlying storage, %util at 100% and the avgu-sz will probably be approaching the level of concurrency of your read traffic. Now, assuming that is true, the question is why. So: (1) Are you continually saturating disk or just periodically? (2) If periodically, does the periods of saturation correlate with compaction being done by Cassandra (or for that matter something else)? (3) What is your data set size relative to system memory? What is your system memory and JVM heap size? (Relevant because it is important to look at how much memory the kernel will use for page caching.) As others have mentioned, the amount of reads done on disk for each read form the database (assuming data is not in cache) can be affected by how data is written (e.g., partial row writes etc). That is one thing that can be addressed, as is re-structuring data to allow reading more sequentially (if possible). That only helps along one dimension though - lessening, somewhat, the cost of cold reads. The gains may be limited and the real problem may be that you simply need more memory for caching and/or more IOPS from your storage (i.e., more disks, maybe SSD, etc). If on the other hand you're normally completely fine and you're just seeing periods of saturation associated with compaction, this may be mitigated by software improvements by possibly rate limiting reads and/or writes during compaction and avoiding buffer cache thrashing. There's a JIRA ticket for direct I/O (https://issues.apache.org/jira/browse/CASSANDRA-1470). I don't think there's a JIRA ticket for rate limiting, but I suspect, since you're doing time series data, that you're not storing very large values - and I would expect compaction to be CPU bound rather than being close to saturate disk. In either case, please do report back as it's interesting to figure out what kind of performance issues people are seeing. -- / Peter Schuller -- Alaa Zubaidi PDF Solutions, Inc. 333 West San Carlos Street, Suite 700 San Jose, CA 95110 USA Tel: 408-283-5639 (or 408-280-7900
Re: SSD vs. HDD
SSDs are not reliable after a (relatively-low compared to spinning disk) number of writes. They may significantly boost performance if used on the journal storage, but will suffer short lifetimes for highly-random write patterns. In general, plan to replace them frequently. Whether they are worth it, given the performance improvement over the cost of replacement x hardware x logistics is generally a calculus problem. It's difficult to make a generic rationale for or against them. You might be better off in general by throwing more memory at your servers, and isolating your random access from your journaled data. Is there any pattern to your reads and writes/deletes? If it is fully random across your keys, then you have the worst-case scenario. Sometimes you can impose access patterns or structural patterns in your app which make caching more effective. Good questions to ask about your data access: Is there a user session which shows an access pattern to proximal data? Are there sets of access which always happen close together? Are there keys or maps which add extra indirection? I'm not familiar with your situation. I was just providing some general ideas.. Jonathan Shook On Wed, Nov 3, 2010 at 2:32 PM, Alaa Zubaidi alaa.zuba...@pdf.com wrote: Hi, we have a continuous high throughput writes, read and delete, and we are trying to find the best hardware. Is using SSD for Cassandra improves performance? Did any one compare SSD vs. HDD? and any recommendations on SSDs? Thanks, Alaa
Re: SSD vs. HDD
SSD will not generally improve your write performance very much, but they can significantly improve read performance. You do *not* want to waste an SSD on the commitlog drive, as even a slow HDD can write sequentially very quickly. For the data drive, they might make sense. As Jonathan talks about, it has a lot to do with your access patterns. If you either: (1) delete parts of rows (2) update parts of rows, or (3) insert new columns into existing rows frequently, you'll end up with rows spread across several SSTables (which are on disk). This means that each read may require several seeks, which are very slow for HDDs, but are very quick for SSDs. Of course, the randomness of what rows you access is also important, but Jonathan did a good job of covering that. Don't forget about the effects of caching here, too. The only way to tell if it is cost-effective is to test your particular access patterns (using a configured stress.py test or, preferably, your actual application). - Tyler On Wed, Nov 3, 2010 at 3:44 PM, Jonathan Shook jsh...@gmail.com wrote: SSDs are not reliable after a (relatively-low compared to spinning disk) number of writes. They may significantly boost performance if used on the journal storage, but will suffer short lifetimes for highly-random write patterns. In general, plan to replace them frequently. Whether they are worth it, given the performance improvement over the cost of replacement x hardware x logistics is generally a calculus problem. It's difficult to make a generic rationale for or against them. You might be better off in general by throwing more memory at your servers, and isolating your random access from your journaled data. Is there any pattern to your reads and writes/deletes? If it is fully random across your keys, then you have the worst-case scenario. Sometimes you can impose access patterns or structural patterns in your app which make caching more effective. Good questions to ask about your data access: Is there a user session which shows an access pattern to proximal data? Are there sets of access which always happen close together? Are there keys or maps which add extra indirection? I'm not familiar with your situation. I was just providing some general ideas.. Jonathan Shook On Wed, Nov 3, 2010 at 2:32 PM, Alaa Zubaidi alaa.zuba...@pdf.com wrote: Hi, we have a continuous high throughput writes, read and delete, and we are trying to find the best hardware. Is using SSD for Cassandra improves performance? Did any one compare SSD vs. HDD? and any recommendations on SSDs? Thanks, Alaa
Re: SSD vs. HDD
Thanks for the reply. I am having time out errors while reading. I have 5 CFs but two CFs with high write/read. The data is organized in time series rows, in CF1 the new rows are read every 10 seconds and then the whole rows are deleted, While in CF2 the rows are read in different time range slices and eventually deleted may be after few hours. Thanks On 11/3/2010 1:58 PM, Tyler Hobbs wrote: SSD will not generally improve your write performance very much, but they can significantly improve read performance. You do *not* want to waste an SSD on the commitlog drive, as even a slow HDD can write sequentially very quickly. For the data drive, they might make sense. As Jonathan talks about, it has a lot to do with your access patterns. If you either: (1) delete parts of rows (2) update parts of rows, or (3) insert new columns into existing rows frequently, you'll end up with rows spread across several SSTables (which are on disk). This means that each read may require several seeks, which are very slow for HDDs, but are very quick for SSDs. Of course, the randomness of what rows you access is also important, but Jonathan did a good job of covering that. Don't forget about the effects of caching here, too. The only way to tell if it is cost-effective is to test your particular access patterns (using a configured stress.py test or, preferably, your actual application). - Tyler On Wed, Nov 3, 2010 at 3:44 PM, Jonathan Shookjsh...@gmail.com wrote: SSDs are not reliable after a (relatively-low compared to spinning disk) number of writes. They may significantly boost performance if used on the journal storage, but will suffer short lifetimes for highly-random write patterns. In general, plan to replace them frequently. Whether they are worth it, given the performance improvement over the cost of replacement x hardware x logistics is generally a calculus problem. It's difficult to make a generic rationale for or against them. You might be better off in general by throwing more memory at your servers, and isolating your random access from your journaled data. Is there any pattern to your reads and writes/deletes? If it is fully random across your keys, then you have the worst-case scenario. Sometimes you can impose access patterns or structural patterns in your app which make caching more effective. Good questions to ask about your data access: Is there a user session which shows an access pattern to proximal data? Are there sets of access which always happen close together? Are there keys or maps which add extra indirection? I'm not familiar with your situation. I was just providing some general ideas.. Jonathan Shook On Wed, Nov 3, 2010 at 2:32 PM, Alaa Zubaidialaa.zuba...@pdf.com wrote: Hi, we have a continuous high throughput writes, read and delete, and we are trying to find the best hardware. Is using SSD for Cassandra improves performance? Did any one compare SSD vs. HDD? and any recommendations on SSDs? Thanks, Alaa -- Alaa Zubaidi PDF Solutions, Inc. 333 West San Carlos Street, Suite 700 San Jose, CA 95110 USA Tel: 408-283-5639 (or 408-280-7900 x5639) fax: 408-938-6479 email: alaa.zuba...@pdf.com
Re: SSD vs. HDD
Ah. Point taken on the random access SSD performance. I was trying to emphasize the relative failure rates given the two scenarios. I didn't mean to imply that SSD random access performance was not a likely improvement here, just that it was a complicated trade-off in the grand scheme of things.. Thanks for catching my goof. On Wed, Nov 3, 2010 at 3:58 PM, Tyler Hobbs ty...@riptano.com wrote: SSD will not generally improve your write performance very much, but they can significantly improve read performance. You do *not* want to waste an SSD on the commitlog drive, as even a slow HDD can write sequentially very quickly. For the data drive, they might make sense. As Jonathan talks about, it has a lot to do with your access patterns. If you either: (1) delete parts of rows (2) update parts of rows, or (3) insert new columns into existing rows frequently, you'll end up with rows spread across several SSTables (which are on disk). This means that each read may require several seeks, which are very slow for HDDs, but are very quick for SSDs. Of course, the randomness of what rows you access is also important, but Jonathan did a good job of covering that. Don't forget about the effects of caching here, too. The only way to tell if it is cost-effective is to test your particular access patterns (using a configured stress.py test or, preferably, your actual application). - Tyler On Wed, Nov 3, 2010 at 3:44 PM, Jonathan Shook jsh...@gmail.com wrote: SSDs are not reliable after a (relatively-low compared to spinning disk) number of writes. They may significantly boost performance if used on the journal storage, but will suffer short lifetimes for highly-random write patterns. In general, plan to replace them frequently. Whether they are worth it, given the performance improvement over the cost of replacement x hardware x logistics is generally a calculus problem. It's difficult to make a generic rationale for or against them. You might be better off in general by throwing more memory at your servers, and isolating your random access from your journaled data. Is there any pattern to your reads and writes/deletes? If it is fully random across your keys, then you have the worst-case scenario. Sometimes you can impose access patterns or structural patterns in your app which make caching more effective. Good questions to ask about your data access: Is there a user session which shows an access pattern to proximal data? Are there sets of access which always happen close together? Are there keys or maps which add extra indirection? I'm not familiar with your situation. I was just providing some general ideas.. Jonathan Shook On Wed, Nov 3, 2010 at 2:32 PM, Alaa Zubaidi alaa.zuba...@pdf.com wrote: Hi, we have a continuous high throughput writes, read and delete, and we are trying to find the best hardware. Is using SSD for Cassandra improves performance? Did any one compare SSD vs. HDD? and any recommendations on SSDs? Thanks, Alaa
Re: SSD vs. HDD
How high is high and how much data do you have (Cassandra disk usage). Regards, Terje On 4 Nov 2010, at 04:32, Alaa Zubaidi alaa.zuba...@pdf.com wrote: Hi, we have a continuous high throughput writes, read and delete, and we are trying to find the best hardware. Is using SSD for Cassandra improves performance? Did any one compare SSD vs. HDD? and any recommendations on SSDs? Thanks, Alaa
Re: SSD vs. HDD
around 1800 col/sec per node, 3kb columns, reading is the same. Data will be deleted after 4 hours. On 11/3/2010 5:00 PM, Terje Marthinussen wrote: How high is high and how much data do you have (Cassandra disk usage). Regards, Terje On 4 Nov 2010, at 04:32, Alaa Zubaidialaa.zuba...@pdf.com wrote: Hi, we have a continuous high throughput writes, read and delete, and we are trying to find the best hardware. Is using SSD for Cassandra improves performance? Did any one compare SSD vs. HDD? and any recommendations on SSDs? Thanks, Alaa -- Alaa Zubaidi PDF Solutions, Inc. 333 West San Carlos Street, Suite 700 San Jose, CA 95110 USA Tel: 408-283-5639 (or 408-280-7900 x5639) fax: 408-938-6479 email: alaa.zuba...@pdf.com
Re: SSD vs. HDD
Some comments inline... On Wed, Nov 3, 2010 at 1:44 PM, Jonathan Shook jsh...@gmail.com wrote: SSDs are not reliable after a (relatively-low compared to spinning disk) number of writes. They may significantly boost performance if used on the journal storage, but will suffer short lifetimes for highly-random write patterns. I agree with this statement in general, however, my understanding is that Cassandra NEVER does random writes. It only ever does large sequential writes. Cassandra could potentially be the perfect use case for MLC (multi-level-cell) SSD's. On Wed, Nov 3, 2010 at 1:58 PM, Tyler Hobbs ty...@riptano.com wrote: You do *not* want to waste an SSD on the commitlog drive, as even a slow HDD can write sequentially very quickly. For the data drive, they might make sense. Totally agreed, we do a few thousand writes per second on a single 7200rpm SATA disk. On Wed, Nov 3, 2010 at 5:20 PM, Alaa Zubaidi alaa.zuba...@pdf.com wrote: around 1800 col/sec per node, 3kb columns, reading is the same. Data will be deleted after 4 hours. Hmm, only keeping the data for 4 hours could present some unique challenges with Cassandra since it does not actually delete the data (it only tombstones the data). There are several factors that play into when exactly the data actually goes away -Eric
