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Transport Client Versus Node Clientedit
If you are using Java, you may wonder when to use the transport client versus the node client. As discussed at the beginning of the book, the transport client acts as a communication layer between the cluster and your application. It knows the API and can automatically round-robin between nodes, sniff the cluster for you, and more. But it is external to the cluster, similar to the REST clients.
The node client, on the other hand, is actually a node within the cluster (but does not hold data, and cannot become master). Because it is a node, it knows the entire cluster state (where all the nodes reside, which shards live in which nodes, and so forth). This means it can execute APIs with one less network hop.
There are uses-cases for both clients:
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The transport client is ideal if you want to decouple your application from the cluster. For example, if your application quickly creates and destroys connections to the cluster, a transport client is much "lighter" than a node client, since it is not part of a cluster.
Similarly, if you need to create thousands of connections, you don’t want to have thousands of node clients join the cluster. The TC will be a better choice.
- On the flipside, if you need only a few long-lived, persistent connection objects to the cluster, a node client can be a bit more efficient since it knows the cluster layout. But it ties your application into the cluster, so it may pose problems from a firewall perspective.
- 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