I am using elasticsearch to index logs from an automation run of test cases. I am creating an index for each of the runs (that can have from 1000 to million events). I create about 200 indices per day. Is this a good methodology to create an index for each run or should I just have 1 index and then put all the logs from multiple runs into this index?
The amount of data is huge and so I chose separate indices. I am expecting 200 logs everyday each with 1million events. Please help me
Depends how long you want to retain your data and the size of your cluster. At 200 indices per day, each with lots of associated files, you're looking at a lot of file handles. So, that doesn't sound like it would scale beyond a few weeks or months on a very small cluster since you'll be running out of file handles.
A better strategy might be to do what logstash does by default which is to create a new index every day. Then your next choice will be to play with the number of shards and nodes in the cluster. Assuming you want to store a worst case of 200M log entries per day on a 3 or 5 node cluster, probably the default of 5 shards is fine. If you go for more nodes, you'll probably want more shards so that each shard is smaller. Also consider using elasticsearch curator to e.g. close older indices and optimize them.
Related
I need to load 1.2 billion documents in the elasticsearch. As of today we have 6 nodes in the cluster. To equally distribute the shards among the 6 nodes I have mentioned the number of shards to be 42. I use spark and it takes me almost 3 days load the index. The shards distribution looks so off.
The node6 only has two shards in it while node 2 has almost 10 shards. The size distribution is also not even. Some shards are 114.6gb while some are just 870mb within the same node.
I have tried to figure out the solution too. I can include the
index.routing.allocation.total_shards_per_node: 7
while creating the index and make it evenly distribute. Will forcing the designated amount of shards in the node, crash the node if there is not enough resource available?
I want to size the shards evenly. My index size is 900 gb apprx. I want each shards to be atleast 20 gb. Could I use the following setting while creating the index?
max_primary_shard_size: 25gb
Is setting up max shard size only possible through ilm policy and will I require roll over policy for that ? I am not too familiar with the ilm. Sorry if this does not make sense.
The main reason I am trying to optimize the index is because I am getting timeout error on my application when I am querying the elastic search. I know I can increase my timeout time in my application and do some query optimization, but first I want to optimize my index and make my application as fast as possible.
I load the index only one time and do not write any documents to it after onetime load. For additional data, which i load every 15 days, I create a different index and use an alias name on the both the indexes to query. Other than sharding if there is any suggestion to optimize my indexes I will really appreciate it. It takes me 3 days just to load the data so it is quite difficult to experiment.
are you using custom routing values in your indexing approach? that might explain the shard size differences.
and if you aren't already, disable replicas and refreshes when doing your bulk index, as that will speed things up
finally your shard size of 20gig is probably a little low, I would suggest doubling that size, aiming for <50gig
Background
We're designing the architecture of a new system using Elasticsearch now, and we plan to use Elastic Cloud based on reviews contrasting their service with AWS's, and self-hosting on an EC2 instance. As we design the system, I'm trying to learn from a small test project my team deployed on Elastic Cloud 6 months ago. While I've spent a lot of time reading the Elasticsearch Docs, Elasticsearch: The Definitive Guide, and Elastic Cloud's Docs, there are some concepts here that I'm still not understanding.
Our Test Project's issues
Our test project uses the default of 5 primary shards and 1 replica shard per primary. It was configured using the default deployment options on Elastic Cloud with a single one node, currently with 2GB of memory. Because there is only one node, and because replica shards are never assigned to the same node as their primary shard (reason 2), none of the replicas are getting assigned. Also, this project uses time-based data, and is creating one index per account per day, resulting in about 10 indexes per day (or 100 shards), and over time, the proverbial Kagillion Shards. This system was only ever meant to have several months of data on it at a time, so the solution has been to manually delete old data when memory on this deployment runs out.
The New System
Our new system is meant to have 5 years worth of time based-data on it, which is projected to grow to 250 GB in size. The current implementation uses a single index for the time-based data, with 6 primary shards and 1 replica per primary. This decision was made based on reading that a single shard should aim for a maximum of 30GB in size.
Questions
Our old system had one node with too many indexes (over 100) and too many shards (over 1000), and it seems like our new one is being designed with too few (one index for 5+ years of data). It seems a better indexing strategy according to the time-based data recommendations would be to create one index per week or month? That being said, according to another answer on SO the optimal number of indexes per node is 1, so what is the utility in creating multiple indices for time-based data in the first place if we're only running on one node?
How does one add a node to an ES deployment in Elastic Cloud? Currently all of the replica nodes in the test project are unassigned, because the deployment only has one node. There is a slider which allows you to easily choose the memory of each node in a deployment (between 1GB and 250B), however I see no way to add multiple nodes, which is confusing because it seems like basic functionality for Elasticsearch.
Our test project's node has restarted several times, always when there is lots of old data on the node, and therefore memory pressure. The solution has been to delete old data (as the test project was only meant to have several months of data at a time), but it appears the node didn't lose data when it restarted. Why would this be?
Our test project has taken no snapshots, which are supposed to happen automatically on Elastic Cloud every 30 minutes. I've asked their support about this, but just curious to see if anyone knows what could cause this and how to resolve it?
Our test project uses the default of 5 primary shards and 1 replica shard per primary. It was configured using the default deployment options on Elastic Cloud with a single one node
Clearly, on a single node, you cannot have replicas. So your index should have been configured with 0 replicas and you can do it dynamically to get your cluster back to green (PUT index/_settings {"index.number_of_replicas": 0}), simple as that.
Also, this project uses time-based data, and is creating one index per account per day, resulting in about 10 indexes per day (or 100 shards)
I cannot tell if 50 new primary shards (10 index) per day were reasonable or not because you don't give any information regarding the volume of data in your test project. But it's probably too many.
It seems a better indexing strategy according to the time-based data recommendations would be to create one index per week or month?
Having five years worth of data in a single index is perfectly possible, it doesn't really depend on how old the data is, but on how big it grows. You mention 250GB and also that you know a shard shouldn't grow over 30GB (and that again depends on the spec of your hardware underneath, more on that later), but since you have only 6 shards for that index, it means that each shard will grow over 40GB (which is ok according to this), but to be on the safe side, you should probably increase to 8-9 shards, or you split your data into yearly/monthly indices.
The 30GB-ish limit per shard is also dependent on how much heap your nodes have. If you have nodes with 2GB heap, then having 30GB shards is clearly too big. Since you're on ES Cloud and you plan to have 250GB of data, you must have chosen a node capacity of 16GB heap + 384GB storage (or bigger). So with 16GB heap, it's reasonable to have 30GB shards, but you'll need several nodes in my opinion. You can verify how many nodes you have using GET _cat/nodes?v.
That being said, according to another answer on SO the optimal number of indexes per node is 1...
What Chris is saying is a theoretical/ideal setting, which is almost never possible/advisable/desired to do in reality. You do want to have several shards in your index and the reason is that when your data grows, you want to be able to scale to more than one node, that's the whole point of ES, otherwise you'd be better off embedding the Lucene library directly in your project.
..., so what is the utility in creating multiple indices for time-based data in the first place if we're only running on one node?
First check how many nodes you have in your cluster using GET _cat/nodes?v, but clearly if you're assigned a single node for 250GB of data split on 6-8 shards, a single node is not ideal, indeed.
How does one add a node to an ES deployment in Elastic Cloud?
Right now, you can't. However, at the last Elastic{ON} conference, Elastic announced that it will be possible to pick the number of nodes or the kind of deployment (hot/warm, etc) you want to set up.
Currently all of the replica nodes in the test project are unassigned, because the deployment only has one node.
You don't really need replicas in a test project, right?
The solution has been to delete old data (as the test project was only meant to have several months of data at a time), but it appears the node didn't lose data when it restarted. Why would this be?
How did you delete the data? Between the time you deleted the data and before the node restarted, did you witness that the data was indeed gone?
Our test project has taken no snapshots, which are supposed to happen automatically on Elastic Cloud every 30 minutes.
This is weird, since on ES cloud your cluster generally gets snapshotted every 30 minutes. What do you see under Deployments > cluster-id > Elasticsearch > Snapshots? What does the ES Cloud support say about it? What do you get when running GET _cat/repositories?v and GET _cat/snapshots/found-snapshots?v? (update your question with the results)
I'm using Elasticsearch as a centralized logging platform . As most examples show I've been logging to multiple indexes time-stamped by day (e.g. logmessages-2017-04-14)
However, I only have a single node setup that contains all these daily indexes. Would I be better off just logging to a single logmessages index on this single node?
Since I only have a single node I have replicas set to 0 and shards set to 1 for each daily index. I'm indexing about 100,000 documents per month.
The answer is "no".
A logging use case always has a retention period defined, meaning after some time you don't need those logs anymore and you need to delete them. This is the same with Elasticsearch indices: when the retention period has been reached that log is deleted.
With time based indices, you delete one day's index and that's it. It's much much more preferred to delete entire indices than individual documents from indices.
We have java application with embedded Elasticsearch in a cluster of 14 nodes. All the data resides in a central database, and they are indexed in elasticsearch for querying. A full reindex can be done at any time.
The system are very query-heavy, the amount of writes are small. The number of documents will not be higher than, say, 300.000.
The size of each document varies greatly, from just a couple of ids, to extracted text from e.g word-documents of several pages.
I want to make sure that in case of a total breakdown, it should be sufficient that one or two nodes are available for the system to work.
Write consistency should not be a problem since the master copy of the data is in the database, and it seems that ES is capable of resolving conflicting data by using the newest version (which should be all right in our case)
My first though is to use 1 shard, and 13 replicas. This will naturally ensure that all nodes have access to all data. This could also be accomplished by having 2 shards / 13 replicas, so this yield that to ensure that all data is available, the number of replicas should be the number of nodes - 1, not depending on the number of shards (which could be anything).
If the requirement of number of nodes are reduced to "2 nodes should be up at any time", then a shards / replica distribution of "x/number of nodes - 2" should be sufficient.
So, for the question:
Asserting the above setup and that my thoughts is correct, would a setup with 1 shard / 13 replicas make sense or would there be anything to gain by adding more shards and run e.g a 4 shards/13 replicas setup?
After a good bit of research and talking to ES-gurus;
As long as the shard size is small enough, the most efficient way of setting up this cluster would indeed be 1 shard only, with 13 replicas. I have not been able to pinpoint the threshold size of the shard for this starting to perform worse.
If the index is big... you will need more than one shard (if you want perfomance). Do You really need 13 replica? When you put only 2 replicas, ES manage that to keep it that way, if the principal node fail, ES will create a new reply. May be you will need a balancer node too.
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I'm in the middle of attempting to replace a Solr setup with Elasticsearch. This is a new setup, which has not yet seen production, so I have lots of room to fiddle with things and get them working well.
I have very, very large amounts of data. I'm indexing some live data and holding onto it for 7 days (by using the _ttl field). I do not store any data in the index (and disabled the _source field). I expect my index to stabilize around 20 billion rows. I will be putting this data into 2-3 named indexes. Search performance so far with up to a few billion rows is totally acceptable, but indexing performance is an issue.
I am a bit confused about how ES uses shards internally. I have created two ES nodes, each with a separate data directory, each with 8 indexes and 1 replica. When I look at the cluster status, I only see one shard and one replica for each node. Doesn't each node keep multiple indexes running internally? (Checking the on-disk storage location shows that there is definitely only one Lucene index present). -- Resolved, as my index setting was not picked up properly from the config. Creating the index using the API and specifying the number of shards and replicas has now produced exactly what I would've expected to see.
Also, I tried running multiple copies of the same ES node (from the same configuration), and it recognizes that there is already a copy running and creates its own working area. These new instances of nodes also seem to only have one index on-disk. -- Now that each node is actually using multiple indices, a single node with many indices is more than sufficient to throttle the entire system, so this is a non-issue.
When do you start additional Elasticsearch nodes, for maximum indexing performance? Should I have many nodes each running with 1 index 1 replica, or fewer nodes with tons of indexes? Is there something I'm missing with my configuration in order to have single nodes doing more work?
Also: Is there any metric for knowing when an HTTP-only node is overloaded? Right now I have one node devoted to HTTP only, but aside from CPU usage, I can't tell if it's doing OK or not. When is it time to start additional HTTP nodes and split up your indexing software to point to the various nodes?
Let's clarify the terminology a little first:
Node: an Elasticsearch instance running (a java process). Usually every node runs on its own machine.
Cluster: one or more nodes with the same cluster name.
Index: more or less like a database.
Type: more or less like a database table.
Shard: effectively a lucene index. Every index is composed of one or more shards. A shard can be a primary shard (or simply shard) or a replica.
When you create an index you can specify the number of shards and number of replicas per shard. The default is 5 primary shards and 1 replica per shard. The shards are automatically evenly distributed over the cluster. A replica shard will never be allocated on the same machine where the related primary shard is.
What you see in the cluster status is weird, I'd suggest to check your index settings using the using the get settings API. Looks like you configured only one shard, but anyway you should see more shards if you have more than one index. If you need more help you can post the output that you get from elasticsearch.
How many shards and replicas you use really depends on your data, the way you access them and the number of available nodes/servers. It's best practice to overallocate shards a little in order to redistribute them in case you add more nodes to your cluster, since you can't (for now) change the number of shards once you created the index. Otherwise you can always change the number of shards if you are willing to do a complete reindex of your data.
Every additional shard comes with a cost since each shard is effectively a Lucene instance. The maximum number of shards that you can have per machine really depends on the hardware available and your data as well. Good to know that having 100 indexes with each one shard or one index with 100 shards is really the same since you'd have 100 lucene instances in both cases.
Of course at query time if you want to query a single elasticsearch index composed of 100 shards elasticsearch would need to query them all in order to get proper results (unless you used a specific routing for your documents to then query only a specific shard). This would have a performance cost.
You can easily check the state of your cluster and nodes using the Cluster Nodes Info API through which you can check a lot of useful information, all you need in order to know whether your nodes are running smoothly or not. Even easier, there are a couple of plugins to check those information through a nice user interface (which internally uses the elasticsearch APIs anyway): paramedic and bigdesk.