Re: Recommendations needed for large ELK system design
Hi Mark, I've done more investigating and it seems that a Client (AKA Query) node cannot also be a Master node. As it says here http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/modules-discovery-zen.html#master-election *Nodes can be excluded from becoming a master by setting node.master to false. Note, once a node is a client node (node.client set to true), it will not be allowed to become a master (node.master is automatically set to false).* And from the elasticsearch.yml config file it says: *# 2. You want this node to only serve as a master: to not store any data and # to have free resources. This will be the coordinator of your cluster. # #node.master: true #node.data: false # # 3. You want this node to be neither master nor data node, but # to act as a search load balancer (fetching data from nodes, # aggregating results, etc.) # #node.master: false #node.data: false* So I'm wondering how exactly you set up your client nodes to also be master nodes. It seems like a master node can only either be purely a master or master + data. Regards, Alex On Thursday, 31 July 2014 23:57:26 UTC+1, Mark Walkom wrote: 1 - Curator FTW. 2 - Masters handle cluster state, shard allocation and a whole bunch of other stuff around managing the cluster and it's members and data. A node that is master and data set to false is considered a search node. But the role of being a master is not onerous, so it made sense for us to double up the roles. We then just round robin any queries to these three masters. 3 - Yes, butit's entirely dependent on your environment. If you're happy with that and you can get the go-ahead then see where it takes you. 4 - Quorum is automatic and having the n/2+1 means that the majority of nodes will have to take place in an election, which reduces the possibility of split brain. If you set the discovery settings then you are also essentially setting the quorum settings. Regards, Mark Walkom Infrastructure Engineer Campaign Monitor email: ma...@campaignmonitor.com javascript: web: www.campaignmonitor.com On 31 July 2014 22:27, Alex alex@gmail.com javascript: wrote: Hello Mark, Thank you for your reply, it certainly helps to clarify many things. Of course I have some new questions for you! 1. I haven't looked into it much yet but I'm guessing Curator can handle different index naming schemes. E.g. logs-2014.06.30 and stats-2014.06.30. We'd actually be wanting to store the stats data for 2 years and logs for 90 days so it would indeed be helpful to split the data into different index sets. Do you use Curator? 2. You say that you have 3 masters that also handle queries... but I thought all masters did was handle queries? What is a master node that *doesn't* handle queries? Should we have search load balancer nodes? AKA not master and not data nodes. 3. In the interests of reducing the number of node combinations for us to test out would you say, then, that 3 master (and query(??)) only nodes, and the 6 1TB data only nodes would be good? 4. Quorum and split brain are new to me. This webpage http://blog.trifork.com/2013/10/24/how-to-avoid-the-split-brain-problem-in-elasticsearch/ about split brain recommends setting *discovery.zen.minimum_master_nodes* equal to *N/2 + 1*. This formula is similar to the one given in the documentation for quorum http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-index_.html#index-consistency: index operations only succeed if a quorum (replicas/2+1) of active shards are available. I completely understand the split brain issue, but not quorum. Is quorum handled automatically or should I change some settings? Thanks again for your help, we appreciate your time and knowledge! Regards, Alex On Thursday, 31 July 2014 05:57:35 UTC+1, Mark Walkom wrote: 1 - Looks ok, but why two replicas? You're chewing up disk for what reason? Extra comments below. 2 - It's personal preference really and depends on how your end points send to redis. 3 - 4GB for redis will cache quite a lot of data if you're only doing 50 events p/s (ie hours or even days based on what I've seen). 4 - No, spread it out to all the nodes. More on that below though. 5 - No it will handle that itself. Again, more on that below though. Suggestions; Set your indexes to (factors of) 6 shards, ie one per node, it spreads query performance. I say factors of in that you can set it to 12 shards per index to start and easily scale the node count and still spread the load. Split your stats and your log data into different indexes, it'll make management and retention easier. You can consider a master only node or (ideally) three that also handle queries. Preferably have an uneven number of master eligible nodes, whether you make them VMs or
Re: Recommendations needed for large ELK system design
Ok thank you Mark, you've been extremely helpful and we now have a better idea about what we're doing! -Alex On Thursday, 31 July 2014 23:57:26 UTC+1, Mark Walkom wrote: 1 - Curator FTW. 2 - Masters handle cluster state, shard allocation and a whole bunch of other stuff around managing the cluster and it's members and data. A node that is master and data set to false is considered a search node. But the role of being a master is not onerous, so it made sense for us to double up the roles. We then just round robin any queries to these three masters. 3 - Yes, butit's entirely dependent on your environment. If you're happy with that and you can get the go-ahead then see where it takes you. 4 - Quorum is automatic and having the n/2+1 means that the majority of nodes will have to take place in an election, which reduces the possibility of split brain. If you set the discovery settings then you are also essentially setting the quorum settings. Regards, Mark Walkom Infrastructure Engineer Campaign Monitor email: ma...@campaignmonitor.com javascript: web: www.campaignmonitor.com On 31 July 2014 22:27, Alex alex@gmail.com javascript: wrote: Hello Mark, Thank you for your reply, it certainly helps to clarify many things. Of course I have some new questions for you! 1. I haven't looked into it much yet but I'm guessing Curator can handle different index naming schemes. E.g. logs-2014.06.30 and stats-2014.06.30. We'd actually be wanting to store the stats data for 2 years and logs for 90 days so it would indeed be helpful to split the data into different index sets. Do you use Curator? 2. You say that you have 3 masters that also handle queries... but I thought all masters did was handle queries? What is a master node that *doesn't* handle queries? Should we have search load balancer nodes? AKA not master and not data nodes. 3. In the interests of reducing the number of node combinations for us to test out would you say, then, that 3 master (and query(??)) only nodes, and the 6 1TB data only nodes would be good? 4. Quorum and split brain are new to me. This webpage http://blog.trifork.com/2013/10/24/how-to-avoid-the-split-brain-problem-in-elasticsearch/ about split brain recommends setting *discovery.zen.minimum_master_nodes* equal to *N/2 + 1*. This formula is similar to the one given in the documentation for quorum http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-index_.html#index-consistency: index operations only succeed if a quorum (replicas/2+1) of active shards are available. I completely understand the split brain issue, but not quorum. Is quorum handled automatically or should I change some settings? Thanks again for your help, we appreciate your time and knowledge! Regards, Alex On Thursday, 31 July 2014 05:57:35 UTC+1, Mark Walkom wrote: 1 - Looks ok, but why two replicas? You're chewing up disk for what reason? Extra comments below. 2 - It's personal preference really and depends on how your end points send to redis. 3 - 4GB for redis will cache quite a lot of data if you're only doing 50 events p/s (ie hours or even days based on what I've seen). 4 - No, spread it out to all the nodes. More on that below though. 5 - No it will handle that itself. Again, more on that below though. Suggestions; Set your indexes to (factors of) 6 shards, ie one per node, it spreads query performance. I say factors of in that you can set it to 12 shards per index to start and easily scale the node count and still spread the load. Split your stats and your log data into different indexes, it'll make management and retention easier. You can consider a master only node or (ideally) three that also handle queries. Preferably have an uneven number of master eligible nodes, whether you make them VMs or physicals, that way you can ensure quorum is reached with minimal fuss and stop split brain. If you use VMs for master + query nodes then you might want to look at load balancing the queries via an external service. To give you an idea, we have a 27 node cluster - 3 masters that also handle queries and 24 data nodes. Masters are 8GB with small disks, data nodes are 60GB (30 heap) and 512GB disk. We're running with one replica and have 11TB of logging data. At a high level we're running out of disk more than heap or CPU and we're very write heavy, with an average of 1K events p/s and comparatively minimal reads. Regards, Mark Walkom Infrastructure Engineer Campaign Monitor email: ma...@campaignmonitor.com web: www.campaignmonitor.com On 31 July 2014 01:35, Alex alex@gmail.com wrote: Hello, We wish to set up an entire ELK system with the following features: - Input from Logstash shippers located on 400 Linux VMs. Only a handful of log sources on each
Re: Recommendations needed for large ELK system design
Hello Mark, Thank you for your reply, it certainly helps to clarify many things. Of course I have some new questions for you! 1. I haven't looked into it much yet but I'm guessing Curator can handle different index naming schemes. E.g. logs-2014.06.30 and stats-2014.06.30. We'd actually be wanting to store the stats data for 2 years and logs for 90 days so it would indeed be helpful to split the data into different index sets. Do you use Curator? 2. You say that you have 3 masters that also handle queries... but I thought all masters did was handle queries? What is a master node that *doesn't* handle queries? Should we have search load balancer nodes? AKA not master and not data nodes. 3. In the interests of reducing the number of node combinations for us to test out would you say, then, that 3 master (and query(??)) only nodes, and the 6 1TB data only nodes would be good? 4. Quorum and split brain are new to me. This webpage http://blog.trifork.com/2013/10/24/how-to-avoid-the-split-brain-problem-in-elasticsearch/ about split brain recommends setting *discovery.zen.minimum_master_nodes* equal to *N/2 + 1*. This formula is similar to the one given in the documentation for quorum http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-index_.html#index-consistency: index operations only succeed if a quorum (replicas/2+1) of active shards are available. I completely understand the split brain issue, but not quorum. Is quorum handled automatically or should I change some settings? Thanks again for your help, we appreciate your time and knowledge! Regards, Alex On Thursday, 31 July 2014 05:57:35 UTC+1, Mark Walkom wrote: 1 - Looks ok, but why two replicas? You're chewing up disk for what reason? Extra comments below. 2 - It's personal preference really and depends on how your end points send to redis. 3 - 4GB for redis will cache quite a lot of data if you're only doing 50 events p/s (ie hours or even days based on what I've seen). 4 - No, spread it out to all the nodes. More on that below though. 5 - No it will handle that itself. Again, more on that below though. Suggestions; Set your indexes to (factors of) 6 shards, ie one per node, it spreads query performance. I say factors of in that you can set it to 12 shards per index to start and easily scale the node count and still spread the load. Split your stats and your log data into different indexes, it'll make management and retention easier. You can consider a master only node or (ideally) three that also handle queries. Preferably have an uneven number of master eligible nodes, whether you make them VMs or physicals, that way you can ensure quorum is reached with minimal fuss and stop split brain. If you use VMs for master + query nodes then you might want to look at load balancing the queries via an external service. To give you an idea, we have a 27 node cluster - 3 masters that also handle queries and 24 data nodes. Masters are 8GB with small disks, data nodes are 60GB (30 heap) and 512GB disk. We're running with one replica and have 11TB of logging data. At a high level we're running out of disk more than heap or CPU and we're very write heavy, with an average of 1K events p/s and comparatively minimal reads. Regards, Mark Walkom Infrastructure Engineer Campaign Monitor email: ma...@campaignmonitor.com javascript: web: www.campaignmonitor.com On 31 July 2014 01:35, Alex alex@gmail.com javascript: wrote: Hello, We wish to set up an entire ELK system with the following features: - Input from Logstash shippers located on 400 Linux VMs. Only a handful of log sources on each VM. - Data retention for 30 days, which is roughly 2TB of data in indexed ES JSON form (not including replica shards) - Estimated input data rate of 50 messages per second at peak hours. Mostly short or medium length one-line messages but there will be Java traces and very large service responses (in the form of XML) to deal with too. - The entire system would be on our company LAN. - The stored data will be a mix of application logs (info, errors etc) and server stats (CPU, memory usage etc) and would mostly be accessed through Kibana. This is our current plan: - Have the LS shippers perform minimal parsing (but would do multiline). Have them point to two load-balanced servers containing Redis and LS indexers (which would do all parsing). - 2 replica shards for each index, which ramps the total data storage up to 6TB - ES cluster spread over 6 nodes. Each node is 1TB in size - LS indexers pointing to cluster. So I have a couple questions regarding the setup and would greatly appreciate the advice of someone with experience! 1. Does the balance between the number of nodes, the number of replica
Re: Recommendations needed for large ELK system design
1 - Curator FTW. 2 - Masters handle cluster state, shard allocation and a whole bunch of other stuff around managing the cluster and it's members and data. A node that is master and data set to false is considered a search node. But the role of being a master is not onerous, so it made sense for us to double up the roles. We then just round robin any queries to these three masters. 3 - Yes, butit's entirely dependent on your environment. If you're happy with that and you can get the go-ahead then see where it takes you. 4 - Quorum is automatic and having the n/2+1 means that the majority of nodes will have to take place in an election, which reduces the possibility of split brain. If you set the discovery settings then you are also essentially setting the quorum settings. Regards, Mark Walkom Infrastructure Engineer Campaign Monitor email: ma...@campaignmonitor.com web: www.campaignmonitor.com On 31 July 2014 22:27, Alex alex.mon...@gmail.com wrote: Hello Mark, Thank you for your reply, it certainly helps to clarify many things. Of course I have some new questions for you! 1. I haven't looked into it much yet but I'm guessing Curator can handle different index naming schemes. E.g. logs-2014.06.30 and stats-2014.06.30. We'd actually be wanting to store the stats data for 2 years and logs for 90 days so it would indeed be helpful to split the data into different index sets. Do you use Curator? 2. You say that you have 3 masters that also handle queries... but I thought all masters did was handle queries? What is a master node that *doesn't* handle queries? Should we have search load balancer nodes? AKA not master and not data nodes. 3. In the interests of reducing the number of node combinations for us to test out would you say, then, that 3 master (and query(??)) only nodes, and the 6 1TB data only nodes would be good? 4. Quorum and split brain are new to me. This webpage http://blog.trifork.com/2013/10/24/how-to-avoid-the-split-brain-problem-in-elasticsearch/ about split brain recommends setting *discovery.zen.minimum_master_nodes* equal to *N/2 + 1*. This formula is similar to the one given in the documentation for quorum http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-index_.html#index-consistency: index operations only succeed if a quorum (replicas/2+1) of active shards are available. I completely understand the split brain issue, but not quorum. Is quorum handled automatically or should I change some settings? Thanks again for your help, we appreciate your time and knowledge! Regards, Alex On Thursday, 31 July 2014 05:57:35 UTC+1, Mark Walkom wrote: 1 - Looks ok, but why two replicas? You're chewing up disk for what reason? Extra comments below. 2 - It's personal preference really and depends on how your end points send to redis. 3 - 4GB for redis will cache quite a lot of data if you're only doing 50 events p/s (ie hours or even days based on what I've seen). 4 - No, spread it out to all the nodes. More on that below though. 5 - No it will handle that itself. Again, more on that below though. Suggestions; Set your indexes to (factors of) 6 shards, ie one per node, it spreads query performance. I say factors of in that you can set it to 12 shards per index to start and easily scale the node count and still spread the load. Split your stats and your log data into different indexes, it'll make management and retention easier. You can consider a master only node or (ideally) three that also handle queries. Preferably have an uneven number of master eligible nodes, whether you make them VMs or physicals, that way you can ensure quorum is reached with minimal fuss and stop split brain. If you use VMs for master + query nodes then you might want to look at load balancing the queries via an external service. To give you an idea, we have a 27 node cluster - 3 masters that also handle queries and 24 data nodes. Masters are 8GB with small disks, data nodes are 60GB (30 heap) and 512GB disk. We're running with one replica and have 11TB of logging data. At a high level we're running out of disk more than heap or CPU and we're very write heavy, with an average of 1K events p/s and comparatively minimal reads. Regards, Mark Walkom Infrastructure Engineer Campaign Monitor email: ma...@campaignmonitor.com web: www.campaignmonitor.com On 31 July 2014 01:35, Alex alex@gmail.com wrote: Hello, We wish to set up an entire ELK system with the following features: - Input from Logstash shippers located on 400 Linux VMs. Only a handful of log sources on each VM. - Data retention for 30 days, which is roughly 2TB of data in indexed ES JSON form (not including replica shards) - Estimated input data rate of 50 messages per second at peak hours. Mostly short or medium length one-line messages but there will be Java
Re: Recommendations needed for large ELK system design
You can further simplify your architecture by using rsyslog with omelasticsearch instead of LS. This might be handy: http://blog.sematext.com/2013/07/01/recipe-rsyslog-elasticsearch-kibana/ Otis -- Performance Monitoring * Log Analytics * Search Analytics Solr Elasticsearch Support * http://sematext.com/ tel: +1 347 480 1610 fax: +1 718 679 9190 On Friday, August 1, 2014 12:57:26 AM UTC+2, Mark Walkom wrote: 1 - Curator FTW. 2 - Masters handle cluster state, shard allocation and a whole bunch of other stuff around managing the cluster and it's members and data. A node that is master and data set to false is considered a search node. But the role of being a master is not onerous, so it made sense for us to double up the roles. We then just round robin any queries to these three masters. 3 - Yes, butit's entirely dependent on your environment. If you're happy with that and you can get the go-ahead then see where it takes you. 4 - Quorum is automatic and having the n/2+1 means that the majority of nodes will have to take place in an election, which reduces the possibility of split brain. If you set the discovery settings then you are also essentially setting the quorum settings. Regards, Mark Walkom Infrastructure Engineer Campaign Monitor email: ma...@campaignmonitor.com javascript: web: www.campaignmonitor.com On 31 July 2014 22:27, Alex alex@gmail.com javascript: wrote: Hello Mark, Thank you for your reply, it certainly helps to clarify many things. Of course I have some new questions for you! 1. I haven't looked into it much yet but I'm guessing Curator can handle different index naming schemes. E.g. logs-2014.06.30 and stats-2014.06.30. We'd actually be wanting to store the stats data for 2 years and logs for 90 days so it would indeed be helpful to split the data into different index sets. Do you use Curator? 2. You say that you have 3 masters that also handle queries... but I thought all masters did was handle queries? What is a master node that *doesn't* handle queries? Should we have search load balancer nodes? AKA not master and not data nodes. 3. In the interests of reducing the number of node combinations for us to test out would you say, then, that 3 master (and query(??)) only nodes, and the 6 1TB data only nodes would be good? 4. Quorum and split brain are new to me. This webpage http://blog.trifork.com/2013/10/24/how-to-avoid-the-split-brain-problem-in-elasticsearch/ about split brain recommends setting *discovery.zen.minimum_master_nodes* equal to *N/2 + 1*. This formula is similar to the one given in the documentation for quorum http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-index_.html#index-consistency: index operations only succeed if a quorum (replicas/2+1) of active shards are available. I completely understand the split brain issue, but not quorum. Is quorum handled automatically or should I change some settings? Thanks again for your help, we appreciate your time and knowledge! Regards, Alex On Thursday, 31 July 2014 05:57:35 UTC+1, Mark Walkom wrote: 1 - Looks ok, but why two replicas? You're chewing up disk for what reason? Extra comments below. 2 - It's personal preference really and depends on how your end points send to redis. 3 - 4GB for redis will cache quite a lot of data if you're only doing 50 events p/s (ie hours or even days based on what I've seen). 4 - No, spread it out to all the nodes. More on that below though. 5 - No it will handle that itself. Again, more on that below though. Suggestions; Set your indexes to (factors of) 6 shards, ie one per node, it spreads query performance. I say factors of in that you can set it to 12 shards per index to start and easily scale the node count and still spread the load. Split your stats and your log data into different indexes, it'll make management and retention easier. You can consider a master only node or (ideally) three that also handle queries. Preferably have an uneven number of master eligible nodes, whether you make them VMs or physicals, that way you can ensure quorum is reached with minimal fuss and stop split brain. If you use VMs for master + query nodes then you might want to look at load balancing the queries via an external service. To give you an idea, we have a 27 node cluster - 3 masters that also handle queries and 24 data nodes. Masters are 8GB with small disks, data nodes are 60GB (30 heap) and 512GB disk. We're running with one replica and have 11TB of logging data. At a high level we're running out of disk more than heap or CPU and we're very write heavy, with an average of 1K events p/s and comparatively minimal reads. Regards, Mark Walkom Infrastructure Engineer Campaign Monitor email: ma...@campaignmonitor.com web:
Recommendations needed for large ELK system design
Hello, We wish to set up an entire ELK system with the following features: - Input from Logstash shippers located on 400 Linux VMs. Only a handful of log sources on each VM. - Data retention for 30 days, which is roughly 2TB of data in indexed ES JSON form (not including replica shards) - Estimated input data rate of 50 messages per second at peak hours. Mostly short or medium length one-line messages but there will be Java traces and very large service responses (in the form of XML) to deal with too. - The entire system would be on our company LAN. - The stored data will be a mix of application logs (info, errors etc) and server stats (CPU, memory usage etc) and would mostly be accessed through Kibana. This is our current plan: - Have the LS shippers perform minimal parsing (but would do multiline). Have them point to two load-balanced servers containing Redis and LS indexers (which would do all parsing). - 2 replica shards for each index, which ramps the total data storage up to 6TB - ES cluster spread over 6 nodes. Each node is 1TB in size - LS indexers pointing to cluster. So I have a couple questions regarding the setup and would greatly appreciate the advice of someone with experience! 1. Does the balance between the number of nodes, the number of replica shards, and storage size of each node seem about right? We use high-performance equipment and would expect minimal downtime. 2. What is your recommendation for the system design of the LS indexers and Redis? I've seen various designs with each indexer assigned to a single Redis, or all indexers reading from all Redises. 3. Leading from the previous question, what would your recommend data size for the Redis servers be? 4. Not sure what to do about master/data nodes. Assuming all the nodes are on identical hardware would it be beneficial to have a node which is only a master which would only handle requests? 5. Do we need to do any additional load balancing on the ES nodes? We are open to any and all suggestions. We have not yet committed to any particular design so can change if needed. Thank you for your time and responses, Alex -- You received this message because you are subscribed to the Google Groups elasticsearch group. To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/b0aee66a-35bb-4770-927b-d9c7e13ad9fc%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
Re: Recommendations needed for large ELK system design
1 - Looks ok, but why two replicas? You're chewing up disk for what reason? Extra comments below. 2 - It's personal preference really and depends on how your end points send to redis. 3 - 4GB for redis will cache quite a lot of data if you're only doing 50 events p/s (ie hours or even days based on what I've seen). 4 - No, spread it out to all the nodes. More on that below though. 5 - No it will handle that itself. Again, more on that below though. Suggestions; Set your indexes to (factors of) 6 shards, ie one per node, it spreads query performance. I say factors of in that you can set it to 12 shards per index to start and easily scale the node count and still spread the load. Split your stats and your log data into different indexes, it'll make management and retention easier. You can consider a master only node or (ideally) three that also handle queries. Preferably have an uneven number of master eligible nodes, whether you make them VMs or physicals, that way you can ensure quorum is reached with minimal fuss and stop split brain. If you use VMs for master + query nodes then you might want to look at load balancing the queries via an external service. To give you an idea, we have a 27 node cluster - 3 masters that also handle queries and 24 data nodes. Masters are 8GB with small disks, data nodes are 60GB (30 heap) and 512GB disk. We're running with one replica and have 11TB of logging data. At a high level we're running out of disk more than heap or CPU and we're very write heavy, with an average of 1K events p/s and comparatively minimal reads. Regards, Mark Walkom Infrastructure Engineer Campaign Monitor email: ma...@campaignmonitor.com web: www.campaignmonitor.com On 31 July 2014 01:35, Alex alex.mon...@gmail.com wrote: Hello, We wish to set up an entire ELK system with the following features: - Input from Logstash shippers located on 400 Linux VMs. Only a handful of log sources on each VM. - Data retention for 30 days, which is roughly 2TB of data in indexed ES JSON form (not including replica shards) - Estimated input data rate of 50 messages per second at peak hours. Mostly short or medium length one-line messages but there will be Java traces and very large service responses (in the form of XML) to deal with too. - The entire system would be on our company LAN. - The stored data will be a mix of application logs (info, errors etc) and server stats (CPU, memory usage etc) and would mostly be accessed through Kibana. This is our current plan: - Have the LS shippers perform minimal parsing (but would do multiline). Have them point to two load-balanced servers containing Redis and LS indexers (which would do all parsing). - 2 replica shards for each index, which ramps the total data storage up to 6TB - ES cluster spread over 6 nodes. Each node is 1TB in size - LS indexers pointing to cluster. So I have a couple questions regarding the setup and would greatly appreciate the advice of someone with experience! 1. Does the balance between the number of nodes, the number of replica shards, and storage size of each node seem about right? We use high-performance equipment and would expect minimal downtime. 2. What is your recommendation for the system design of the LS indexers and Redis? I've seen various designs with each indexer assigned to a single Redis, or all indexers reading from all Redises. 3. Leading from the previous question, what would your recommend data size for the Redis servers be? 4. Not sure what to do about master/data nodes. Assuming all the nodes are on identical hardware would it be beneficial to have a node which is only a master which would only handle requests? 5. Do we need to do any additional load balancing on the ES nodes? We are open to any and all suggestions. We have not yet committed to any particular design so can change if needed. Thank you for your time and responses, Alex -- You received this message because you are subscribed to the Google Groups elasticsearch group. To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/b0aee66a-35bb-4770-927b-d9c7e13ad9fc%40googlegroups.com https://groups.google.com/d/msgid/elasticsearch/b0aee66a-35bb-4770-927b-d9c7e13ad9fc%40googlegroups.com?utm_medium=emailutm_source=footer . For more options, visit https://groups.google.com/d/optout. -- You received this message because you are subscribed to the Google Groups elasticsearch group. To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit
