Re: Efficiently determining if cache keys belong to the localservernode
> How does Ignite ensure requests consistency during rebalancing? Basically, sorts of locking and some retry-on-failure handling. Ignite will make sure that all operations on a partition are finished before moving/evicting it, and will try to switch to another node if the first node that it tried to access has failed. The difference with compute grid APIs (affinityCall/broadcast/execute) is that they're not locking any caches themselves. affinityCall uses a cache key to determine where to execute, but it doesn't use that key after that. Compare it with IgniteCache.Invoke() which does kind of the same thing as affinityCall, but with semantics of "acquiring" a cache key it is called on - and therefore being atomic or transactional in relation to other operations on the same cache. Stan On Tue, Apr 17, 2018 at 10:25 PM, Raymond Wilson <raymond_wil...@trimble.com > wrote: > Agree on the idempotent comments. Many of the requests are aggregative > summarisations so there’ll need to be some additional tracking to detect > double computation and missed computation in these cases. > > > > I understand that Ignite grids respond to requests during rebalancing > operations where partitions may move between nodes over significant time > periods. How does Ignite ensure requests consistency during rebalancing? > > > > *From:* Stanislav Lukyanov [mailto:stanlukya...@gmail.com] > *Sent:* Wednesday, April 18, 2018 12:26 AM > > *To:* user@ignite.apache.org > *Subject:* RE: Efficiently determining if cache keys belong to the > localservernode > > > > > Is the failure mode of a node changing primality for a key during an > affinity co-located compute function handled by Ignite automatically for > other contexts? > > Are you asking whether or not affinityCall() would handle that? If so, > then no, not really – once the job is sent to a node, it is out. To handle > that Ignite would need to be able to stop the job, revert its changes and > restart it on another node – which is not possible in general, of course. > > > > > Is there an event or similar facility to hook into to gain a > notification that this has occurred (and so re-run the computation to > ensure the correct result)? > > You could listen to EVT_NODE_LEFT, EVT_NODE_FAILED and EVT_NODE_JOINED to > track topology changes, but it seems rather complex and fragile to me > > Instead I would try to make the computations idempotent (i.e. to make sure > that processing the same key on two nodes doesn’t lead to inconsistency), > and track which keys were processed to be able to restart the computation > on the unprocessed ones (if any). > > > > Stan > > > > *From: *Raymond Wilson <raymond_wil...@trimble.com> > *Sent: *17 апреля 2018 г. 14:01 > *To: *user@ignite.apache.org > *Subject: *RE: Efficiently determining if cache keys belong to the > localservernode > > > > Hi Stan > > > > Thanks for the additional pointers. > > > > Is the failure mode of a node changing primality for a key during an > affinity co-located compute function handled by Ignite automatically for > other contexts? Is there an event or similar facility to hook into to gain > a notification that this has occurred (and so re-run the computation to > ensure the correct result)? > > > > Thanks, > > Raymond. > > > > > > *From:* Stanislav Lukyanov [mailto:stanlukya...@gmail.com] > *Sent:* Tuesday, April 17, 2018 10:42 PM > *To:* user@ignite.apache.org > *Subject:* RE: Efficiently determining if cache keys belong to the local > servernode > > > > Hi Raymond, > > > > OK, I see, batching the requests makes sense. > > Have you looked at the ICacheAffinity interface? It provides a way to > query Ignite about the key-to-node mappings, > > without dealing with partitions yourself. > > The call > > ignite.GetAffinity(“cache”).MapKeysToNodes(keys) > > is suitable to split the request into batches on the client side. > > The call > > ignite.GetAffinity(“cache”).IsPrimary(key, ignite.GetCluster(). > GetLocalNode()) > > is suitable to determine if a the current node is primary for the key. > > > > This way you don’t need to cache affinity mappings – you just always use > the current mappings of the node. > > However, you still need to make sure you can handle affinity mappings > changing while your jobs are running. > > One can imagine situations when two nodes process the same key (because > both were primary at different times), > > or no nodes processed a key (e.g. because a new node has joined, became > primary for the key but didn’t receive the broadcast). > > > > Thanks, > >
RE: Efficiently determining if cache keys belong to the localservernode
Agree on the idempotent comments. Many of the requests are aggregative summarisations so there’ll need to be some additional tracking to detect double computation and missed computation in these cases. I understand that Ignite grids respond to requests during rebalancing operations where partitions may move between nodes over significant time periods. How does Ignite ensure requests consistency during rebalancing? *From:* Stanislav Lukyanov [mailto:stanlukya...@gmail.com] *Sent:* Wednesday, April 18, 2018 12:26 AM *To:* user@ignite.apache.org *Subject:* RE: Efficiently determining if cache keys belong to the localservernode > Is the failure mode of a node changing primality for a key during an affinity co-located compute function handled by Ignite automatically for other contexts? Are you asking whether or not affinityCall() would handle that? If so, then no, not really – once the job is sent to a node, it is out. To handle that Ignite would need to be able to stop the job, revert its changes and restart it on another node – which is not possible in general, of course. > Is there an event or similar facility to hook into to gain a notification that this has occurred (and so re-run the computation to ensure the correct result)? You could listen to EVT_NODE_LEFT, EVT_NODE_FAILED and EVT_NODE_JOINED to track topology changes, but it seems rather complex and fragile to me Instead I would try to make the computations idempotent (i.e. to make sure that processing the same key on two nodes doesn’t lead to inconsistency), and track which keys were processed to be able to restart the computation on the unprocessed ones (if any). Stan *From: *Raymond Wilson <raymond_wil...@trimble.com> *Sent: *17 апреля 2018 г. 14:01 *To: *user@ignite.apache.org *Subject: *RE: Efficiently determining if cache keys belong to the localservernode Hi Stan Thanks for the additional pointers. Is the failure mode of a node changing primality for a key during an affinity co-located compute function handled by Ignite automatically for other contexts? Is there an event or similar facility to hook into to gain a notification that this has occurred (and so re-run the computation to ensure the correct result)? Thanks, Raymond. *From:* Stanislav Lukyanov [mailto:stanlukya...@gmail.com] *Sent:* Tuesday, April 17, 2018 10:42 PM *To:* user@ignite.apache.org *Subject:* RE: Efficiently determining if cache keys belong to the local servernode Hi Raymond, OK, I see, batching the requests makes sense. Have you looked at the ICacheAffinity interface? It provides a way to query Ignite about the key-to-node mappings, without dealing with partitions yourself. The call ignite.GetAffinity(“cache”).MapKeysToNodes(keys) is suitable to split the request into batches on the client side. The call ignite.GetAffinity(“cache”).IsPrimary(key, ignite.GetCluster().GetLocalNode()) is suitable to determine if a the current node is primary for the key. This way you don’t need to cache affinity mappings – you just always use the current mappings of the node. However, you still need to make sure you can handle affinity mappings changing while your jobs are running. One can imagine situations when two nodes process the same key (because both were primary at different times), or no nodes processed a key (e.g. because a new node has joined, became primary for the key but didn’t receive the broadcast). Thanks, Stan *From: *Raymond Wilson <raymond_wil...@trimble.com> *Sent: *16 апреля 2018 г. 23:36 *To: *user@ignite.apache.org *Subject: *RE: Efficiently determining if cache keys belong to the local servernode Hi Stan, Your understanding is correct. I'm aware of the AffinityRun and AffinityCall methods, and their simple key limitation. My use case may require 100,000 or more elements of information to be processed, so I don't want to call AffinityRun/Call that often. Each of these elements is identified by a key that is very efficiently encoded into the request (at the ~1 bit per key level) Further, each of those elements identifies work units that in themselves could have 100,000 or more different elements to be processed. One approach would be to explicitly break up the request into smaller ones, each targeted at a server node. But that requires the requestor to have intimate knowledge of the composition of the grid resources deployed, which is not desirable. The approach I'm looking into here is to have each server node receive the same request via Cluster.Broadcast(), and for those nodes to determine which elements in the overall request via the Key -> Partition affinity mapping. The mapping itself is very efficient, and as I noted in my original post determining the partition -> node map seems simple enough to do. I'm unsure of the performance of requesting that mapping for every request, versus caching it and adding watchers for rebalan
RE: Efficiently determining if cache keys belong to the localservernode
> Is the failure mode of a node changing primality for a key during an affinity > co-located compute function handled by Ignite automatically for other > contexts? Are you asking whether or not affinityCall() would handle that? If so, then no, not really – once the job is sent to a node, it is out. To handle that Ignite would need to be able to stop the job, revert its changes and restart it on another node – which is not possible in general, of course. > Is there an event or similar facility to hook into to gain a notification > that this has occurred (and so re-run the computation to ensure the correct > result)? You could listen to EVT_NODE_LEFT, EVT_NODE_FAILED and EVT_NODE_JOINED to track topology changes, but it seems rather complex and fragile to me Instead I would try to make the computations idempotent (i.e. to make sure that processing the same key on two nodes doesn’t lead to inconsistency), and track which keys were processed to be able to restart the computation on the unprocessed ones (if any). Stan From: Raymond Wilson Sent: 17 апреля 2018 г. 14:01 To: user@ignite.apache.org Subject: RE: Efficiently determining if cache keys belong to the localservernode Hi Stan Thanks for the additional pointers. Is the failure mode of a node changing primality for a key during an affinity co-located compute function handled by Ignite automatically for other contexts? Is there an event or similar facility to hook into to gain a notification that this has occurred (and so re-run the computation to ensure the correct result)? Thanks, Raymond. From: Stanislav Lukyanov [mailto:stanlukya...@gmail.com] Sent: Tuesday, April 17, 2018 10:42 PM To: user@ignite.apache.org Subject: RE: Efficiently determining if cache keys belong to the local servernode Hi Raymond, OK, I see, batching the requests makes sense. Have you looked at the ICacheAffinity interface? It provides a way to query Ignite about the key-to-node mappings, without dealing with partitions yourself. The call ignite.GetAffinity(“cache”).MapKeysToNodes(keys) is suitable to split the request into batches on the client side. The call ignite.GetAffinity(“cache”).IsPrimary(key, ignite.GetCluster().GetLocalNode()) is suitable to determine if a the current node is primary for the key. This way you don’t need to cache affinity mappings – you just always use the current mappings of the node. However, you still need to make sure you can handle affinity mappings changing while your jobs are running. One can imagine situations when two nodes process the same key (because both were primary at different times), or no nodes processed a key (e.g. because a new node has joined, became primary for the key but didn’t receive the broadcast). Thanks, Stan From: Raymond Wilson Sent: 16 апреля 2018 г. 23:36 To: user@ignite.apache.org Subject: RE: Efficiently determining if cache keys belong to the local servernode Hi Stan, Your understanding is correct. I'm aware of the AffinityRun and AffinityCall methods, and their simple key limitation. My use case may require 100,000 or more elements of information to be processed, so I don't want to call AffinityRun/Call that often. Each of these elements is identified by a key that is very efficiently encoded into the request (at the ~1 bit per key level) Further, each of those elements identifies work units that in themselves could have 100,000 or more different elements to be processed. One approach would be to explicitly break up the request into smaller ones, each targeted at a server node. But that requires the requestor to have intimate knowledge of the composition of the grid resources deployed, which is not desirable. The approach I'm looking into here is to have each server node receive the same request via Cluster.Broadcast(), and for those nodes to determine which elements in the overall request via the Key -> Partition affinity mapping. The mapping itself is very efficient, and as I noted in my original post determining the partition -> node map seems simple enough to do. I'm unsure of the performance of requesting that mapping for every request, versus caching it and adding watchers for rebalancing and topology change events to invalidate that cache mapping as needed (and how to wire those up). Thanks, Raymond. -Original Message- From: Stanislav Lukyanov [mailto:stanlukya...@gmail.com] Sent: Tuesday, April 17, 2018 12:02 AM To: user@ignite.apache.org Subject: RE: Efficiently determining if cache keys belong to the local server node // Bcc’ing off dev@ignite list for now as it seems to be rather a user-space discussion. Hi, Let me take a step back first. It seems a bit like an XY problem (https://meta.stackexchange.com/questions/66377/what-is-the-xy-problem), so I’d like to clarify the goals before diving into your current solution. AFAIU you want to process certain entries in your cache locally o
