WARNING: The 2.x versions of Elasticsearch have passed their EOL dates. If you are running a 2.x version, we strongly advise you to upgrade.
This documentation is no longer maintained and may be removed. For the latest information, see the current Elasticsearch documentation.
File Descriptors and MMapedit
Lucene uses a very large number of files. At the same time, Elasticsearch uses a large number of sockets to communicate between nodes and HTTP clients. All of this requires available file descriptors.
Sadly, many modern Linux distributions ship with a paltry 1,024 file descriptors allowed per process. This is far too low for even a small Elasticsearch node, let alone one that is handling hundreds of indices.
You should increase your file descriptor count to something very large, such as 64,000. This process is irritatingly difficult and highly dependent on your particular OS and distribution. Consult the documentation for your OS to determine how best to change the allowed file descriptor count.
Once you think you’ve changed it, check Elasticsearch to make sure it really does have enough file descriptors:
{ "cluster_name": "elasticsearch", "nodes": { "nLd81iLsRcqmah-cuHAbaQ": { "timestamp": 1471516160318, "name": "Marsha Rosenberg", "transport_address": "127.0.0.1:9300", "host": "127.0.0.1", "ip": [ "127.0.0.1:9300", "NONE" ], "process": { "timestamp": 1471516160318, "open_file_descriptors": 155, "max_file_descriptors": 10240, "cpu": { "percent": 0, "total_in_millis": 25084 }, "mem": { "total_virtual_in_bytes": 5221900288 } } } } }
The |
Elasticsearch also uses a mix of NioFS and MMapFS for the various files. Ensure that you configure the maximum map count so that there is ample virtual memory available for mmapped files. This can be set temporarily:
sysctl -w vm.max_map_count=262144
Or you can set it permanently by modifying vm.max_map_count
setting in your /etc/sysctl.conf
.
- Elasticsearch - The Definitive Guide:
- Foreword
- Preface
- Getting Started
- You Know, for Search…
- Installing and Running Elasticsearch
- Talking to Elasticsearch
- Document Oriented
- Finding Your Feet
- Indexing Employee Documents
- Retrieving a Document
- Search Lite
- Search with Query DSL
- More-Complicated Searches
- Full-Text Search
- Phrase Search
- Highlighting Our Searches
- Analytics
- Tutorial Conclusion
- Distributed Nature
- Next Steps
- Life Inside a Cluster
- Data In, Data Out
- What Is a Document?
- Document Metadata
- Indexing a Document
- Retrieving a Document
- Checking Whether a Document Exists
- Updating a Whole Document
- Creating a New Document
- Deleting a Document
- Dealing with Conflicts
- Optimistic Concurrency Control
- Partial Updates to Documents
- Retrieving Multiple Documents
- Cheaper in Bulk
- Distributed Document Store
- Searching—The Basic Tools
- Mapping and Analysis
- Full-Body Search
- Sorting and Relevance
- Distributed Search Execution
- Index Management
- Inside a Shard
- You Know, for Search…
- Search in Depth
- Structured Search
- Full-Text Search
- Multifield Search
- Proximity Matching
- Partial Matching
- Controlling Relevance
- Theory Behind Relevance Scoring
- Lucene’s Practical Scoring Function
- Query-Time Boosting
- Manipulating Relevance with Query Structure
- Not Quite Not
- Ignoring TF/IDF
- function_score Query
- Boosting by Popularity
- Boosting Filtered Subsets
- Random Scoring
- The Closer, The Better
- Understanding the price Clause
- Scoring with Scripts
- Pluggable Similarity Algorithms
- Changing Similarities
- Relevance Tuning Is the Last 10%
- Dealing with Human Language
- Aggregations
- Geolocation
- Modeling Your Data
- Administration, Monitoring, and Deployment