Re: [appengine-java] Re: Can pm.makePersistentAll() help me write 12,000 objects?
Jeff, point taken, but the original poster has been asking for three different requirements: - requirement to do all writes synchronously - sub-some-couple-hundred-millisecond writes - 12k entities being written This just won't scale well if it's common. Messaging users can be done asynchronously, as can the portion crediting friends. I understand the argument that you may want to do this during the lifecycle of the request so the original user gets some kind of feedback backed by a strongly consistent datastore, but this just isn't done. Feedback is usually faked out optimistically, assuming that the writes will all be successful with some cached layer being the only part of the stack being updated inside the request. Thinking of worse case scenarios is a good thought exercise, but it's also a bit too optimistic to design a product assuming all of a Users' friends will play the game and engineer to meet that unrealistic expectation. What are the standard and slightly non-standard use cases? I'd probably look at a solution where I can store the data somewhere associated with the original user for any users not already in the datastore, then retrieve and generate a score for any of that User's friends on first access. Facebook's developer admin tool has some pretty good statistics such as bounce rate, block rate and invitation accept rate that can be used to tune this design. Slightly off topic, but we've been asked before if it was possible to provide different levels of datastore consistency. In some cases I can see the tradeoffs making sense. On Wed, Feb 24, 2010 at 5:52 PM, Jeff Schnitzer j...@infohazard.org wrote: On Wed, Feb 24, 2010 at 1:06 PM, Ikai L (Google) ika...@google.com wrote: My point wasn't necessarily that it wasn't possible. makePersistentAll does use a batch write, and there are definitely sites that can do 12,000+ writes a second (and well above that), but I don't know of any that will attempt to do that in a single request. While it's an interesting thought exercise to see if BigTable can do it through App Engine's interface (hint: it can, globally, easily), I can't think of a single use case for a site to need to do this all the time and with the sub-second requirement. I think it's reasonable to ask why this design exists and why the requirements exist and rethink one or the other. It does seem to be a pretty extreme case, but it's not all that far fetched. It's possible for a Facebook user to have 5,000 friends. Perhaps a user wants to message all 5k of them. I could actually use this right ability now. I would like to add a game mechanic which, when you score some points, you also credit a portion of that to all of a user's friends. Worst case scenario is a 5,000 element read followed by a 5,000 element write. I'm probably going to skip this mechanic for now because I can't afford it - even with the average 200 or so friends. If I want it badly enough, I may ultimately need to move my scoring system offsite. Jeff -- You received this message because you are subscribed to the Google Groups Google App Engine for Java group. To post to this group, send email to google-appengine-j...@googlegroups.com. To unsubscribe from this group, send email to google-appengine-java+unsubscr...@googlegroups.comgoogle-appengine-java%2bunsubscr...@googlegroups.com . For more options, visit this group at http://groups.google.com/group/google-appengine-java?hl=en. -- Ikai Lan Developer Programs Engineer, Google App Engine http://googleappengine.blogspot.com | http://twitter.com/app_engine -- You received this message because you are subscribed to the Google Groups Google App Engine for Java group. To post to this group, send email to google-appengine-j...@googlegroups.com. To unsubscribe from this group, send email to google-appengine-java+unsubscr...@googlegroups.com. For more options, visit this group at http://groups.google.com/group/google-appengine-java?hl=en.
Re: [appengine-java] Re: Can pm.makePersistentAll() help me write 12,000 objects?
I don't think the original poster had a requirement for synchronous writes; he just didn't want to do the writes asynchronously because it involved a lot more code. I'm also perfectly fine with asynchronous writes and a very lax interpretation of consistency. I don't even mind writing extra code. The thing I worry about is the feasibility of a heavy write load and the total cost of it. Unfortunately I really can't describe in detail what I want to do (I normally laugh at this kind of secrecy, but in this case it's warranted). For the game mechanic I'm thinking about, the average-case scenario is not very far from the worst-case scenario. Just a little detail: * There is no requirement that all of a user's friends must be playing the game or even have installed the app to receive points. Welcome to the world of social gaming, you can play without even without knowing it! * There are *lots* of FB users that have 1k friends. Probably millions. More active FB users are likely to have more friends... and more likely to use my app. * Points can be assigned to multiple layers, so the # of updates is (layers * friends). * Tens of thousands of people play this game. It could become hundreds of thousands very soon. If I'm lucky, millions. I would love to implement this game mechanic, but I just can't. Asynchronous or not, it's *way* too expensive on appengine. When it comes time to implement this feature (and it's going to come, I can see the winds blowing), I'm probably going to have to move my scoring system out of appengine. Which is a bit ironic, because one of the main advantages of appengine is scalability. I would *love* to see some sort of super-super-lax and super-super-cheap consistency option for BigTable. Or even an alternative key/value datastore that simply works like a persistent version of memcached. Something that would let me sustain 10k writes/sec without bankrupting me. Jeff On Thu, Feb 25, 2010 at 11:16 AM, Ikai L (Google) ika...@google.com wrote: Jeff, point taken, but the original poster has been asking for three different requirements: - requirement to do all writes synchronously - sub-some-couple-hundred-millisecond writes - 12k entities being written This just won't scale well if it's common. Messaging users can be done asynchronously, as can the portion crediting friends. I understand the argument that you may want to do this during the lifecycle of the request so the original user gets some kind of feedback backed by a strongly consistent datastore, but this just isn't done. Feedback is usually faked out optimistically, assuming that the writes will all be successful with some cached layer being the only part of the stack being updated inside the request. Thinking of worse case scenarios is a good thought exercise, but it's also a bit too optimistic to design a product assuming all of a Users' friends will play the game and engineer to meet that unrealistic expectation. What are the standard and slightly non-standard use cases? I'd probably look at a solution where I can store the data somewhere associated with the original user for any users not already in the datastore, then retrieve and generate a score for any of that User's friends on first access. Facebook's developer admin tool has some pretty good statistics such as bounce rate, block rate and invitation accept rate that can be used to tune this design. Slightly off topic, but we've been asked before if it was possible to provide different levels of datastore consistency. In some cases I can see the tradeoffs making sense. On Wed, Feb 24, 2010 at 5:52 PM, Jeff Schnitzer j...@infohazard.org wrote: On Wed, Feb 24, 2010 at 1:06 PM, Ikai L (Google) ika...@google.com wrote: My point wasn't necessarily that it wasn't possible. makePersistentAll does use a batch write, and there are definitely sites that can do 12,000+ writes a second (and well above that), but I don't know of any that will attempt to do that in a single request. While it's an interesting thought exercise to see if BigTable can do it through App Engine's interface (hint: it can, globally, easily), I can't think of a single use case for a site to need to do this all the time and with the sub-second requirement. I think it's reasonable to ask why this design exists and why the requirements exist and rethink one or the other. It does seem to be a pretty extreme case, but it's not all that far fetched. It's possible for a Facebook user to have 5,000 friends. Perhaps a user wants to message all 5k of them. I could actually use this right ability now. I would like to add a game mechanic which, when you score some points, you also credit a portion of that to all of a user's friends. Worst case scenario is a 5,000 element read followed by a 5,000 element write. I'm probably going to skip this mechanic for now because I can't afford it - even with the average 200 or so friends. If I
[appengine-java] Re: Can pm.makePersistentAll() help me write 12,000 objects?
I think there is a way to grab big chunks of oprations, put them in a queue to be done asynchronously and that would be it. My take would be that using proxies it would be easy to queue any long operation transparently. I've done that with EJBs in the past, I don't see the reason a QueingProxy couldn't be written. Cheers, Guillermo. On 25 feb, 17:02, Jeff Schnitzer j...@infohazard.org wrote: I don't think the original poster had a requirement for synchronous writes; he just didn't want to do the writes asynchronously because it involved a lot more code. I'm also perfectly fine with asynchronous writes and a very lax interpretation of consistency. I don't even mind writing extra code. The thing I worry about is the feasibility of a heavy write load and the total cost of it. Unfortunately I really can't describe in detail what I want to do (I normally laugh at this kind of secrecy, but in this case it's warranted). For the game mechanic I'm thinking about, the average-case scenario is not very far from the worst-case scenario. Just a little detail: * There is no requirement that all of a user's friends must be playing the game or even have installed the app to receive points. Welcome to the world of social gaming, you can play without even without knowing it! * There are *lots* of FB users that have 1k friends. Probably millions. More active FB users are likely to have more friends... and more likely to use my app. * Points can be assigned to multiple layers, so the # of updates is (layers * friends). * Tens of thousands of people play this game. It could become hundreds of thousands very soon. If I'm lucky, millions. I would love to implement this game mechanic, but I just can't. Asynchronous or not, it's *way* too expensive on appengine. When it comes time to implement this feature (and it's going to come, I can see the winds blowing), I'm probably going to have to move my scoring system out of appengine. Which is a bit ironic, because one of the main advantages of appengine is scalability. I would *love* to see some sort of super-super-lax and super-super-cheap consistency option for BigTable. Or even an alternative key/value datastore that simply works like a persistent version of memcached. Something that would let me sustain 10k writes/sec without bankrupting me. Jeff On Thu, Feb 25, 2010 at 11:16 AM, Ikai L (Google) ika...@google.com wrote: Jeff, point taken, but the original poster has been asking for three different requirements: - requirement to do all writes synchronously - sub-some-couple-hundred-millisecond writes - 12k entities being written This just won't scale well if it's common. Messaging users can be done asynchronously, as can the portion crediting friends. I understand the argument that you may want to do this during the lifecycle of the request so the original user gets some kind of feedback backed by a strongly consistent datastore, but this just isn't done. Feedback is usually faked out optimistically, assuming that the writes will all be successful with some cached layer being the only part of the stack being updated inside the request. Thinking of worse case scenarios is a good thought exercise, but it's also a bit too optimistic to design a product assuming all of a Users' friends will play the game and engineer to meet that unrealistic expectation. What are the standard and slightly non-standard use cases? I'd probably look at a solution where I can store the data somewhere associated with the original user for any users not already in the datastore, then retrieve and generate a score for any of that User's friends on first access. Facebook's developer admin tool has some pretty good statistics such as bounce rate, block rate and invitation accept rate that can be used to tune this design. Slightly off topic, but we've been asked before if it was possible to provide different levels of datastore consistency. In some cases I can see the tradeoffs making sense. On Wed, Feb 24, 2010 at 5:52 PM, Jeff Schnitzer j...@infohazard.org wrote: On Wed, Feb 24, 2010 at 1:06 PM, Ikai L (Google) ika...@google.com wrote: My point wasn't necessarily that it wasn't possible. makePersistentAll does use a batch write, and there are definitely sites that can do 12,000+ writes a second (and well above that), but I don't know of any that will attempt to do that in a single request. While it's an interesting thought exercise to see if BigTable can do it through App Engine's interface (hint: it can, globally, easily), I can't think of a single use case for a site to need to do this all the time and with the sub-second requirement. I think it's reasonable to ask why this design exists and why the requirements exist and rethink one or the other. It does seem to be a pretty extreme case, but it's not all that far fetched. It's possible
Re: [appengine-java] Re: Can pm.makePersistentAll() help me write 12,000 objects?
Hi I wrote importing geoip database in JSF. It has like 100k entries. Besides appengine and datastore performance is awful and importing took around 1,5h of total CPU its fairly easy to use TaskQueue. If there is above average processing to be done then i suggest separating task into smaller batches and process them asynchronously. Margus On Thu, Feb 25, 2010 at 3:52 AM, Jeff Schnitzer j...@infohazard.org wrote: On Wed, Feb 24, 2010 at 1:06 PM, Ikai L (Google) ika...@google.com wrote: My point wasn't necessarily that it wasn't possible. makePersistentAll does use a batch write, and there are definitely sites that can do 12,000+ writes a second (and well above that), but I don't know of any that will attempt to do that in a single request. While it's an interesting thought exercise to see if BigTable can do it through App Engine's interface (hint: it can, globally, easily), I can't think of a single use case for a site to need to do this all the time and with the sub-second requirement. I think it's reasonable to ask why this design exists and why the requirements exist and rethink one or the other. It does seem to be a pretty extreme case, but it's not all that far fetched. It's possible for a Facebook user to have 5,000 friends. Perhaps a user wants to message all 5k of them. I could actually use this right ability now. I would like to add a game mechanic which, when you score some points, you also credit a portion of that to all of a user's friends. Worst case scenario is a 5,000 element read followed by a 5,000 element write. I'm probably going to skip this mechanic for now because I can't afford it - even with the average 200 or so friends. If I want it badly enough, I may ultimately need to move my scoring system offsite. Jeff -- You received this message because you are subscribed to the Google Groups Google App Engine for Java group. To post to this group, send email to google-appengine-j...@googlegroups.com. To unsubscribe from this group, send email to google-appengine-java+unsubscr...@googlegroups.comgoogle-appengine-java%2bunsubscr...@googlegroups.com . For more options, visit this group at http://groups.google.com/group/google-appengine-java?hl=en. -- You received this message because you are subscribed to the Google Groups Google App Engine for Java group. To post to this group, send email to google-appengine-j...@googlegroups.com. To unsubscribe from this group, send email to google-appengine-java+unsubscr...@googlegroups.com. For more options, visit this group at http://groups.google.com/group/google-appengine-java?hl=en.
Re: [appengine-java] Re: Can pm.makePersistentAll() help me write 12,000 objects?
Guillermo, Taskqueue items can only be 10K (http://code.google.com/appengine/docs/java/taskqueue/overview.html#Quotas_and_Limits). The basic idea is that if you have more data than that you put it into an entity (in the data-store) and have the task pull it out and process it. It might be that you can persist those 12K entities in a lot of large entities (that are still under 1mByte each), but that is a lot of work for something that will still probably fail. I guess it all depends where the cost is on the puts (indexing, raw writes by bytes, number of items). And if your comeback is memcache, well, I won't even start discussing using a non-persistent, volatile, store like that for temporary storage while you write them to the datastore... in batches using the taskqueue/cron/etc. Really, there needs to be something that can handle the write volume. On Thu, Feb 25, 2010 at 12:08 PM, Guillermo Schwarz guillermo.schw...@gmail.com wrote: I think there is a way to grab big chunks of oprations, put them in a queue to be done asynchronously and that would be it. My take would be that using proxies it would be easy to queue any long operation transparently. I've done that with EJBs in the past, I don't see the reason a QueingProxy couldn't be written. Cheers, Guillermo. On 25 feb, 17:02, Jeff Schnitzer j...@infohazard.org wrote: I don't think the original poster had a requirement for synchronous writes; he just didn't want to do the writes asynchronously because it involved a lot more code. I'm also perfectly fine with asynchronous writes and a very lax interpretation of consistency. I don't even mind writing extra code. The thing I worry about is the feasibility of a heavy write load and the total cost of it. Unfortunately I really can't describe in detail what I want to do (I normally laugh at this kind of secrecy, but in this case it's warranted). For the game mechanic I'm thinking about, the average-case scenario is not very far from the worst-case scenario. Just a little detail: * There is no requirement that all of a user's friends must be playing the game or even have installed the app to receive points. Welcome to the world of social gaming, you can play without even without knowing it! * There are *lots* of FB users that have 1k friends. Probably millions. More active FB users are likely to have more friends... and more likely to use my app. * Points can be assigned to multiple layers, so the # of updates is (layers * friends). * Tens of thousands of people play this game. It could become hundreds of thousands very soon. If I'm lucky, millions. I would love to implement this game mechanic, but I just can't. Asynchronous or not, it's *way* too expensive on appengine. When it comes time to implement this feature (and it's going to come, I can see the winds blowing), I'm probably going to have to move my scoring system out of appengine. Which is a bit ironic, because one of the main advantages of appengine is scalability. I would *love* to see some sort of super-super-lax and super-super-cheap consistency option for BigTable. Or even an alternative key/value datastore that simply works like a persistent version of memcached. Something that would let me sustain 10k writes/sec without bankrupting me. Jeff On Thu, Feb 25, 2010 at 11:16 AM, Ikai L (Google) ika...@google.com wrote: Jeff, point taken, but the original poster has been asking for three different requirements: - requirement to do all writes synchronously - sub-some-couple-hundred-millisecond writes - 12k entities being written This just won't scale well if it's common. Messaging users can be done asynchronously, as can the portion crediting friends. I understand the argument that you may want to do this during the lifecycle of the request so the original user gets some kind of feedback backed by a strongly consistent datastore, but this just isn't done. Feedback is usually faked out optimistically, assuming that the writes will all be successful with some cached layer being the only part of the stack being updated inside the request. Thinking of worse case scenarios is a good thought exercise, but it's also a bit too optimistic to design a product assuming all of a Users' friends will play the game and engineer to meet that unrealistic expectation. What are the standard and slightly non-standard use cases? I'd probably look at a solution where I can store the data somewhere associated with the original user for any users not already in the datastore, then retrieve and generate a score for any of that User's friends on first access. Facebook's developer admin tool has some pretty good statistics such as bounce rate, block rate and invitation accept rate that can be used to tune this design. Slightly off topic, but we've been asked before if it was possible to provide different levels of datastore consistency. In some cases
Re: [appengine-java] Re: Can pm.makePersistentAll() help me write 12,000 objects?
We have an issue for an asynchronous write API for the datastore: http://code.google.com/p/googleappengine/issues/detail?id=2817 This is something that can fit into that model. On Thu, Feb 25, 2010 at 12:26 PM, Scott Hernandez scotthernan...@gmail.comwrote: Guillermo, Taskqueue items can only be 10K ( http://code.google.com/appengine/docs/java/taskqueue/overview.html#Quotas_and_Limits ). The basic idea is that if you have more data than that you put it into an entity (in the data-store) and have the task pull it out and process it. It might be that you can persist those 12K entities in a lot of large entities (that are still under 1mByte each), but that is a lot of work for something that will still probably fail. I guess it all depends where the cost is on the puts (indexing, raw writes by bytes, number of items). And if your comeback is memcache, well, I won't even start discussing using a non-persistent, volatile, store like that for temporary storage while you write them to the datastore... in batches using the taskqueue/cron/etc. Really, there needs to be something that can handle the write volume. On Thu, Feb 25, 2010 at 12:08 PM, Guillermo Schwarz guillermo.schw...@gmail.com wrote: I think there is a way to grab big chunks of oprations, put them in a queue to be done asynchronously and that would be it. My take would be that using proxies it would be easy to queue any long operation transparently. I've done that with EJBs in the past, I don't see the reason a QueingProxy couldn't be written. Cheers, Guillermo. On 25 feb, 17:02, Jeff Schnitzer j...@infohazard.org wrote: I don't think the original poster had a requirement for synchronous writes; he just didn't want to do the writes asynchronously because it involved a lot more code. I'm also perfectly fine with asynchronous writes and a very lax interpretation of consistency. I don't even mind writing extra code. The thing I worry about is the feasibility of a heavy write load and the total cost of it. Unfortunately I really can't describe in detail what I want to do (I normally laugh at this kind of secrecy, but in this case it's warranted). For the game mechanic I'm thinking about, the average-case scenario is not very far from the worst-case scenario. Just a little detail: * There is no requirement that all of a user's friends must be playing the game or even have installed the app to receive points. Welcome to the world of social gaming, you can play without even without knowing it! * There are *lots* of FB users that have 1k friends. Probably millions. More active FB users are likely to have more friends... and more likely to use my app. * Points can be assigned to multiple layers, so the # of updates is (layers * friends). * Tens of thousands of people play this game. It could become hundreds of thousands very soon. If I'm lucky, millions. I would love to implement this game mechanic, but I just can't. Asynchronous or not, it's *way* too expensive on appengine. When it comes time to implement this feature (and it's going to come, I can see the winds blowing), I'm probably going to have to move my scoring system out of appengine. Which is a bit ironic, because one of the main advantages of appengine is scalability. I would *love* to see some sort of super-super-lax and super-super-cheap consistency option for BigTable. Or even an alternative key/value datastore that simply works like a persistent version of memcached. Something that would let me sustain 10k writes/sec without bankrupting me. Jeff On Thu, Feb 25, 2010 at 11:16 AM, Ikai L (Google) ika...@google.com wrote: Jeff, point taken, but the original poster has been asking for three different requirements: - requirement to do all writes synchronously - sub-some-couple-hundred-millisecond writes - 12k entities being written This just won't scale well if it's common. Messaging users can be done asynchronously, as can the portion crediting friends. I understand the argument that you may want to do this during the lifecycle of the request so the original user gets some kind of feedback backed by a strongly consistent datastore, but this just isn't done. Feedback is usually faked out optimistically, assuming that the writes will all be successful with some cached layer being the only part of the stack being updated inside the request. Thinking of worse case scenarios is a good thought exercise, but it's also a bit too optimistic to design a product assuming all of a Users' friends will play the game and engineer to meet that unrealistic expectation. What are the standard and slightly non-standard use cases? I'd probably look at a solution where I can store the data somewhere associated with the original user for any users not already in the datastore, then retrieve and generate a
[appengine-java] Re: Can pm.makePersistentAll() help me write 12,000 objects?
Ikai, Maybe you are right. Maybe not. I'm not an expert in datastore internals, but here is my point of view. This paper claims that Berkeley DB Java edition can insert about 15,000 records per second. http://www.oracle.com/database/docs/bdb-je-architecture-whitepaper.pdf The graphic is on page 22. The main reason they claim to be able to do that is that they don't need to actually sync the write to disk, they can queue the write, update in-memory data and write a log file. Writing the log file is for transactional purposes and it is the only write really needed.That is pretty fast. Cheers, Guillermo. On 24 feb, 16:51, Ikai L (Google) ika...@google.com wrote: I also remember hearing (and this is not verified so don't quote me on this or come after me if I'm wrong) from a friend of mine running KV stores in production that there were issues with certain distributed key/value stores that actually managed to slow down as a function of the number of objects in the store - and Tokyo Tyrant was on his list. A key property of scalable stores is that the opposite of this is true. 12,000 synchronous, serialized writes in a single sub-second request is pretty serious. I am not aware of a single website in the world that does this. On Wed, Feb 24, 2010 at 11:35 AM, Jeff Schnitzer j...@infohazard.orgwrote: I think this is actually an interesting question, and brings up a discussion worth having: Is datastore performance reasonable? I don't want to make this a discussion of reliability, which is a separate issue. It just seems to me that the datastore is actually kinda pokey, taking seconds to write a few hundred entities. When people benchmark Tokyo Tyrant, I hear numbers thrown around like 22,000 writes/second sustained across 1M records: http://blog.hunch.se/2009/02/28-tokyo-cabinet You might argue that the theoretical scalability of BigTable's distributed store is higher... but we're talking about two full orders of magnitude difference. Will I ever near the 100-google-server equivalent load? Could I pay for it if I did? 100 CPUs (measured) running for 1 month is about $7,200. Actual CPU speed is at least twice the measured rate, so a single Tokyo Tyrant is theoretically equivalent to almost $15,000/month of appengine hosting. Ouch. Maybe this isn't an apples to apples comparison. Sure, there aren't extra indexes on those Tyrant entities... but to be honest, few of my entities have extra indexes. What other factors could change this analysis? Thoughts? BTW Tim, you may very well have quite a few indexes on your entities. In JDO, nearly all single fields are indexed by default. You must explicitly add an annotation to your fields to make them unindexed. With Objectify, you can declare your entity as @Indexed or @Unindexed and then use the same annotation on individual fields to override the default. Jeff On Wed, Feb 24, 2010 at 12:43 AM, Tim Cooper tco...@gmail.com wrote: I have been trying to write 12,000 objects in a single page request. These objects are all very small and the total amount of memory is not large. There is no index on these objects - the only GQL queries I make on them are based on the primary key. Ikai has said: That is - if you have to delete or create 150 persistent, indexed objects, you may want to rethink what problems you are trying to solve. So I have been thinking about the problems I'm trying to solve, including looking at the BuddyPoke blog and reading the GAE documentation. I'm trying to populate the database with entries relating to high school timetables. * I could do the writes asynchronously, but that looks like a lot of additional effort. On my C++ app, writing the same information to my laptop drive, this happens in under a second, because the amount of data is actually quite small, but it times out on GAE. * I am using pm.makePersistentAll(), but this doesn't help. * There is no index on the objects - I access them only through the primary key. (I'm pretty sure there's no index - but how can I confirm this via the development server dashboard?) * The objects constitute 12,000 entity groups. I could merge them into fewer entity groups, but there's no natural groupings I could use, so it could get quite complex to introduce a contrived grouping, and also this would complicate the multi-user updating of the objects. The AppEngine team seem to generally recommend using more entity groups, but it's difficult to integrate that advice with the contrary advice to use fewer entity groups for acceptable performance. * I'd be happy if the GAE database was 10 times slower than a non-cloud RDBMS, but the way I'm using it, it's currently not. Does anyone have any advice? -- You received this message because you are subscribed to the Google Groups Google App Engine for Java group. To post
Re: [appengine-java] Re: Can pm.makePersistentAll() help me write 12,000 objects?
Simple key-only writes can definitely do it, but there's a few places where you can introduce overhead: - serialization - network I/O - indexes My point wasn't necessarily that it wasn't possible. makePersistentAll does use a batch write, and there are definitely sites that can do 12,000+ writes a second (and well above that), but I don't know of any that will attempt to do that in a single request. While it's an interesting thought exercise to see if BigTable can do it through App Engine's interface (hint: it can, globally, easily), I can't think of a single use case for a site to need to do this all the time and with the sub-second requirement. I think it's reasonable to ask why this design exists and why the requirements exist and rethink one or the other. On Wed, Feb 24, 2010 at 12:35 PM, Guillermo Schwarz guillermo.schw...@gmail.com wrote: Ikai, Maybe you are right. Maybe not. I'm not an expert in datastore internals, but here is my point of view. This paper claims that Berkeley DB Java edition can insert about 15,000 records per second. http://www.oracle.com/database/docs/bdb-je-architecture-whitepaper.pdf The graphic is on page 22. The main reason they claim to be able to do that is that they don't need to actually sync the write to disk, they can queue the write, update in-memory data and write a log file. Writing the log file is for transactional purposes and it is the only write really needed.That is pretty fast. Cheers, Guillermo. On 24 feb, 16:51, Ikai L (Google) ika...@google.com wrote: I also remember hearing (and this is not verified so don't quote me on this or come after me if I'm wrong) from a friend of mine running KV stores in production that there were issues with certain distributed key/value stores that actually managed to slow down as a function of the number of objects in the store - and Tokyo Tyrant was on his list. A key property of scalable stores is that the opposite of this is true. 12,000 synchronous, serialized writes in a single sub-second request is pretty serious. I am not aware of a single website in the world that does this. On Wed, Feb 24, 2010 at 11:35 AM, Jeff Schnitzer j...@infohazard.org wrote: I think this is actually an interesting question, and brings up a discussion worth having: Is datastore performance reasonable? I don't want to make this a discussion of reliability, which is a separate issue. It just seems to me that the datastore is actually kinda pokey, taking seconds to write a few hundred entities. When people benchmark Tokyo Tyrant, I hear numbers thrown around like 22,000 writes/second sustained across 1M records: http://blog.hunch.se/2009/02/28-tokyo-cabinet You might argue that the theoretical scalability of BigTable's distributed store is higher... but we're talking about two full orders of magnitude difference. Will I ever near the 100-google-server equivalent load? Could I pay for it if I did? 100 CPUs (measured) running for 1 month is about $7,200. Actual CPU speed is at least twice the measured rate, so a single Tokyo Tyrant is theoretically equivalent to almost $15,000/month of appengine hosting. Ouch. Maybe this isn't an apples to apples comparison. Sure, there aren't extra indexes on those Tyrant entities... but to be honest, few of my entities have extra indexes. What other factors could change this analysis? Thoughts? BTW Tim, you may very well have quite a few indexes on your entities. In JDO, nearly all single fields are indexed by default. You must explicitly add an annotation to your fields to make them unindexed. With Objectify, you can declare your entity as @Indexed or @Unindexed and then use the same annotation on individual fields to override the default. Jeff On Wed, Feb 24, 2010 at 12:43 AM, Tim Cooper tco...@gmail.com wrote: I have been trying to write 12,000 objects in a single page request. These objects are all very small and the total amount of memory is not large. There is no index on these objects - the only GQL queries I make on them are based on the primary key. Ikai has said: That is - if you have to delete or create 150 persistent, indexed objects, you may want to rethink what problems you are trying to solve. So I have been thinking about the problems I'm trying to solve, including looking at the BuddyPoke blog and reading the GAE documentation. I'm trying to populate the database with entries relating to high school timetables. * I could do the writes asynchronously, but that looks like a lot of additional effort. On my C++ app, writing the same information to my laptop drive, this happens in under a second, because the amount of data is actually quite small, but it times out on GAE. * I am using pm.makePersistentAll(), but this doesn't help. * There is no index on
[appengine-java] Re: Can pm.makePersistentAll() help me write 12,000 objects?
my experience with a relatively simple application via JDO makePersistentAll() was that I got DataStore Operation Timeout exceptions with batch sizes of approx 200-300 objects ... On Feb 24, 1:48 pm, Guillermo Schwarz guillermo.schw...@gmail.com wrote: I think we can safely assume that the programmer was trying to speed up things a little by writing 12 thousand objects in a single operation. Now if that gets to be faster or slower than writing each object separately, it is a matter of the internal implementation of the data store. I prefer to do no hacks, but OTOH it is better sometimes to be clear bout what you want (API wise). The point here is that the programmer wants to insert 15 thousand objects in a second, you seem to imply that is possible. While it's an interesting thought exercise to see if BigTable can do it through App Engine's interface (hint: it can, globally, easily). I rest my case ;-) Do we need to do anything to test that? Is there anything we could do to help? Cheers, Guillermo. On 24 feb, 18:06, Ikai L (Google) ika...@google.com wrote: Simple key-only writes can definitely do it, but there's a few places where you can introduce overhead: - serialization - network I/O - indexes My point wasn't necessarily that it wasn't possible. makePersistentAll does use a batch write, and there are definitely sites that can do 12,000+ writes a second (and well above that), but I don't know of any that will attempt to do that in a single request. While it's an interesting thought exercise to see if BigTable can do it through App Engine's interface (hint: it can, globally, easily), I can't think of a single use case for a site to need to do this all the time and with the sub-second requirement. I think it's reasonable to ask why this design exists and why the requirements exist and rethink one or the other. On Wed, Feb 24, 2010 at 12:35 PM, Guillermo Schwarz guillermo.schw...@gmail.com wrote: Ikai, Maybe you are right. Maybe not. I'm not an expert in datastore internals, but here is my point of view. This paper claims that Berkeley DB Java edition can insert about 15,000 records per second. http://www.oracle.com/database/docs/bdb-je-architecture-whitepaper.pdf The graphic is on page 22. The main reason they claim to be able to do that is that they don't need to actually sync the write to disk, they can queue the write, update in-memory data and write a log file. Writing the log file is for transactional purposes and it is the only write really needed.That is pretty fast. Cheers, Guillermo. On 24 feb, 16:51, Ikai L (Google) ika...@google.com wrote: I also remember hearing (and this is not verified so don't quote me on this or come after me if I'm wrong) from a friend of mine running KV stores in production that there were issues with certain distributed key/value stores that actually managed to slow down as a function of the number of objects in the store - and Tokyo Tyrant was on his list. A key property of scalable stores is that the opposite of this is true. 12,000 synchronous, serialized writes in a single sub-second request is pretty serious. I am not aware of a single website in the world that does this. On Wed, Feb 24, 2010 at 11:35 AM, Jeff Schnitzer j...@infohazard.org wrote: I think this is actually an interesting question, and brings up a discussion worth having: Is datastore performance reasonable? I don't want to make this a discussion of reliability, which is a separate issue. It just seems to me that the datastore is actually kinda pokey, taking seconds to write a few hundred entities. When people benchmark Tokyo Tyrant, I hear numbers thrown around like 22,000 writes/second sustained across 1M records: http://blog.hunch.se/2009/02/28-tokyo-cabinet You might argue that the theoretical scalability of BigTable's distributed store is higher... but we're talking about two full orders of magnitude difference. Will I ever near the 100-google-server equivalent load? Could I pay for it if I did? 100 CPUs (measured) running for 1 month is about $7,200. Actual CPU speed is at least twice the measured rate, so a single Tokyo Tyrant is theoretically equivalent to almost $15,000/month of appengine hosting. Ouch. Maybe this isn't an apples to apples comparison. Sure, there aren't extra indexes on those Tyrant entities... but to be honest, few of my entities have extra indexes. What other factors could change this analysis? Thoughts? BTW Tim, you may very well have quite a few indexes on your entities. In JDO, nearly all single fields are indexed by default. You must explicitly add an annotation to your fields to make them unindexed. With Objectify, you can declare your entity as
