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---
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layout: default
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title: Go client
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nav_order: 80
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---
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# Go client
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The OpenSearch Go client lets you connect your Go application with the data in your OpenSearch cluster.
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## Setup
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If you're creating a new project:
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```go
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go mod init
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```
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To add the client to your project, import it like any other module:
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```go
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go get github.com/opensearch-project/opensearch-go
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```
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## Sample code
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This sample code creates a client, adds an index with non-default settings, inserts a document, searches for the document, deletes the document, and finally deletes the index:
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```go
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package main
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import (
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"os"
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"context"
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"crypto/tls"
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"fmt"
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opensearch "github.com/opensearch-project/opensearch-go"
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opensearchapi "github.com/opensearch-project/opensearch-go/opensearchapi"
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"net/http"
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"strings"
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)
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const IndexName = "go-test-index1"
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func main() {
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// Initialize the client with SSL/TLS enabled.
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client, err := opensearch.NewClient(opensearch.Config{
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Transport: &http.Transport{
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TLSClientConfig: &tls.Config{InsecureSkipVerify: true},
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},
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Addresses: []string{"https://localhost:9200"},
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Username: "admin", // For testing only. Don't store credentials in code.
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Password: "admin",
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})
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if err != nil {
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fmt.Println("cannot initialize", err)
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os.Exit(1)
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}
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// Print OpenSearch version information on console.
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fmt.Println(client.Info())
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// Define index mapping.
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mapping := strings.NewReader(`{
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'settings': {
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'index': {
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'number_of_shards': 4
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}
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}
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}`)
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// Create an index with non-default settings.
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res := opensearchapi.CreateRequest{
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Index: IndexName,
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Body: mapping,
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}
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fmt.Println("creating index", res)
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// Add a document to the index.
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document := strings.NewReader(`{
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"title": "Moneyball",
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"director": "Bennett Miller",
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"year": "2011"
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}`)
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docId := "1"
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req := opensearchapi.IndexRequest{
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Index: IndexName,
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DocumentID: docId,
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Body: document,
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}
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insertResponse, err := req.Do(context.Background(), client)
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if err != nil {
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fmt.Println("failed to insert document ", err)
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os.Exit(1)
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}
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fmt.Println(insertResponse)
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// Search for the document.
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content := strings.NewReader(`{
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"size": 5,
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"query": {
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"multi_match": {
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"query": "miller",
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"fields": ["title^2", "director"]
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}
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}
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}`)
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search := opensearchapi.SearchRequest{
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Body: content,
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}
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searchResponse, err := search.Do(context.Background(), client)
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if err != nil {
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fmt.Println("failed to search document ", err)
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os.Exit(1)
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}
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fmt.Println(searchResponse)
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// Delete the document.
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delete := opensearchapi.DeleteRequest{
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Index: IndexName,
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DocumentID: docId,
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}
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deleteResponse, err := delete.Do(context.Background(), client)
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if err != nil {
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fmt.Println("failed to delete document ", err)
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os.Exit(1)
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}
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fmt.Println("deleting document")
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fmt.Println(deleteResponse)
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// Delete previously created index.
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deleteIndex := opensearchapi.IndicesDeleteRequest{
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Index: []string{IndexName},
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}
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deleteIndexResponse, err := deleteIndex.Do(context.Background(), client)
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if err != nil {
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fmt.Println("failed to delete index ", err)
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os.Exit(1)
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}
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fmt.Println("deleting index", deleteIndexResponse)
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}
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```
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@@ -1,7 +1,7 @@
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---
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layout: default
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title: Java high-level REST client
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nav_order: 97
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nav_order: 60
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---
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# Java high-level REST client
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@@ -79,11 +79,13 @@ This formula provides a good starting point, but make sure to test with a repres
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For example, for a cluster with 3 data nodes, each with 8G of JVM heap size, a maximum memory percentage of 10% (default), and the entity size of the detector as 1MB: the total number of unique entities supported is (8.096 * 10^9 * 0.1 / 1M ) * 3 = 2429.
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#### Set a shingle size
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#### Set a window size
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Set the number of aggregation intervals from your data stream to consider in a detection window. It’s best to choose this value based on your actual data to see which one leads to the best results for your use case.
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Set the number of aggregation intervals from your data stream to consider in a detection window. It's best to choose this value based on your actual data to see which one leads to the best results for your use case.
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The anomaly detector expects the shingle size to be in the range of 1 and 60. The default shingle size is 8. We recommend that you don't choose 1 unless you have two or more features. Smaller values might increase [recall](https://en.wikipedia.org/wiki/Precision_and_recall) but also false positives. Larger values might be useful for ignoring noise in a signal.
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Based on experiments performed on a wide variety of one-dimensional data streams, we recommend using a window size between 1 and 16. The default window size is 8. If you set the category field for high cardinality, the default window size is 1.
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If you expect missing values in your data or if you want to base the anomalies on the current interval, choose 1. If your data is continuously ingested and you want to base the anomalies on multiple intervals, choose a larger window size.
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#### Preview sample anomalies
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Reference in New Issue
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