is there any way to import a json file(contains 100 documents) in elasticsearch server.?

Is there any way to import a JSON file (contains 100 documents) in elasticsearch server? I want to import a big json file into es-server..

You should use Bulk API. Note that you will need to add a header line before each json document.

$ cat requests
{ "index" : { "_index" : "test", "_type" : "type1", "_id" : "1" } }
{ "field1" : "value1" }
$ curl -s -XPOST localhost:9200/_bulk --data-binary @requests; echo
{"took":7,"items":[{"create":{"_index":"test","_type":"type1","_id":"1","_version":1,"ok":true}}]}

How to Bulk Index Elasticsearch Documents From A JSON File , is there any way to import a json file(contains 100 documents) in elasticsearch as a batch script) on the same server as the brand new ES instance. + '/forms. json')); // and the second set var client = new elasticsearch. Currently there are lots of JSON documents stored in files. Applications log information in JSON files, sensors generate information that's stored in JSON files, and so forth. It's important to be able to read the JSON data stored in files, load the data into SQL Server, and analyze it.

As dadoonet already mentioned, the bulk API is probably the way to go. To transform your file for the bulk protocol, you can use jq.

Assuming the file contains just the documents itself:

$ echo '{"foo":"bar"}{"baz":"qux"}' | 
jq -c '
{ index: { _index: "myindex", _type: "mytype" } },
. '

{"index":{"_index":"myindex","_type":"mytype"}}
{"foo":"bar"}
{"index":{"_index":"myindex","_type":"mytype"}}
{"baz":"qux"}

And if the file contains the documents in a top level list they have to be unwrapped first:

$ echo '[{"foo":"bar"},{"baz":"qux"}]' | 
jq -c '
.[] |
{ index: { _index: "myindex", _type: "mytype" } },
. '

{"index":{"_index":"myindex","_type":"mytype"}}
{"foo":"bar"}
{"index":{"_index":"myindex","_type":"mytype"}}
{"baz":"qux"}

jq's -c flag makes sure that each document is on a line by itself.

If you want to pipe straight to curl, you'll want to use --data-binary @-, and not just -d, otherwise curl will strip the newlines again.

How to use Python helpers to bulk load data into an Elasticsearch , 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 import Python's JSON library for its loads() method import json JSON DATA IN FILE: {"str field": call the function to get the string data containing docs define an empty list for the Elasticsearch docs doc_list� Because, during a typical day, it is likely that you’ll need to do one or more of the following to export Elasticsearch Pandas: export Elasticsearch HTML, export Elasticsearch CSV files, or export Elasticsearch JSON Python documents. When you use Pandas IO Tools Elasticsearch to export Elasticsearch files Python, you can analyze documents faster.

I'm sure someone wants this so I'll make it easy to find.

FYI - This is using Node.js (essentially as a batch script) on the same server as the brand new ES instance. Ran it on 2 files with 4000 items each and it only took about 12 seconds on my shared virtual server. YMMV

var elasticsearch = require('elasticsearch'),
    fs = require('fs'),
    pubs = JSON.parse(fs.readFileSync(__dirname + '/pubs.json')), // name of my first file to parse
    forms = JSON.parse(fs.readFileSync(__dirname + '/forms.json')); // and the second set
var client = new elasticsearch.Client({  // default is fine for me, change as you see fit
  host: 'localhost:9200',
  log: 'trace'
});

for (var i = 0; i < pubs.length; i++ ) {
  client.create({
    index: "epubs", // name your index
    type: "pub", // describe the data thats getting created
    id: i, // increment ID every iteration - I already sorted mine but not a requirement
    body: pubs[i] // *** THIS ASSUMES YOUR DATA FILE IS FORMATTED LIKE SO: [{prop: val, prop2: val2}, {prop:...}, {prop:...}] - I converted mine from a CSV so pubs[i] is the current object {prop:..., prop2:...}
  }, function(error, response) {
    if (error) {
      console.error(error);
      return;
    }
    else {
    console.log(response);  //  I don't recommend this but I like having my console flooded with stuff.  It looks cool.  Like I'm compiling a kernel really fast.
    }
  });
}

for (var a = 0; a < forms.length; a++ ) {  // Same stuff here, just slight changes in type and variables
  client.create({
    index: "epubs",
    type: "form",
    id: a,
    body: forms[a]
  }, function(error, response) {
    if (error) {
      console.error(error);
      return;
    }
    else {
    console.log(response);
    }
  });
}

Hope I can help more than just myself with this. Not rocket science but may save someone 10 minutes.

Cheers

From scratch to search: setup Elasticsearch under 4 minutes, load a , The helper's module – Python helpers to import Elasticsearch data. Using the for loop method, 100 various documents are created. Here's an example of a JSON file containing several Elasticsearch documents: 2020 ObjectRocket, a Rackspace Company � Terms of Service � Legal � Privacy. Pour chaque ligne, nous créons le contrôle JSON Elasticsearch needs (avec L'ID de notre objet original) et créer une deuxième ligne qui est juste notre objet JSON original (.). à ce stade, nous avons notre JSON formaté la façon dont L'API en vrac D'Elasticsearch s'y attend, donc nous l'avons simplement pipe à curl qui le poste à

jq is a lightweight and flexible command-line JSON processor.

Usage:

cat file.json | jq -c '.[] | {"index": {"_index": "bookmarks", "_type": "bookmark", "_id": .id}}, .' | curl -XPOST localhost:9200/_bulk --data-binary @-

We’re taking the file file.json and piping its contents to jq first with the -c flag to construct compact output. Here’s the nugget: We’re taking advantage of the fact that jq can construct not only one but multiple objects per line of input. For each line, we’re creating the control JSON Elasticsearch needs (with the ID from our original object) and creating a second line that is just our original JSON object (.).

At this point we have our JSON formatted the way Elasticsearch’s bulk API expects it, so we just pipe it to curl which POSTs it to Elasticsearch!

Credit goes to Kevin Marsh

Breaking changes in 7.0 | Elasticsearch Reference [7.x], From my perspective the easiest way to start experimenting with Thanks to cluster.initial_master_nodes it has formed a new cluster (with We have indexed a very simple json document with only one field, In any way don't forget to pip install elasticsearch (to install the module) before using the script. Dear community, I have about 1TB of data splitted into many smaller .json files in newline delimited JSON (NDJSON) format. The sizes of the .json files vary between 500MB and 20GB. The files are too big to load using the Bulk API. Of course I could split the files into smaller .json files and then load them using the Bulk API. Is there a more elegant and more efficient way to do this?

Import no, but you can index the documents by using the ES API.

You can use the index api to load each line (using some kind of code to read the file and make the curl calls) or the index bulk api to load them all. Assuming your data file can be formatted to work with it.

Read more here : ES API

A simple shell script would do the trick if you comfortable with shell something like this maybe (not tested):

while read line
do
curl -XPOST 'http://localhost:9200/<indexname>/<typeofdoc>/' -d "$line"
done <myfile.json

Peronally, I would probably use Python either pyes or the elastic-search client.

pyes on github elastic search python client

Stream2es is also very useful for quickly loading data into es and may have a way to simply stream a file in. (I have not tested a file but have used it to load wikipedia doc for es perf testing)

Configure Kibana | Kibana Guide [7.8], An Elasticsearch 7.0 node will not start in the presence of indices created in a version of are distributed across shards depending on how many shards the index has. the number of nested json objects within a single document across all fields The systemd service file /usr/lib/systemd/system/elasticsearch.service was� I'm completely new to Elasticsearch and I've been importing data to Elasticsearch up to this point by manually entering the JSON. I'd like to begin loading in .json files to make things faster and possibly to bulk load in the future. I'm not sure how to do this currently. The .json would be generated from a SQL Server query and formatted as JSON by another layer, and then placed in a folder on

metricbeat.reference.yml | Metricbeat Reference [7.8], The Kibana server reads properties from the kibana.yml file on startup. The location of this file differs depending on how you installed Kibana. Path to a PKCS#12 keystore that contains an X.509 client certificate and it's This value is used to do an inner-join between the document stored in Elasticsearch and the geojson� I'm trying to import some very large JSON files (up to 80GB / file) into Elasticsearch and have tried a couple different approaches but neither is giving me an efficient working solution: Using the BULK API - I received heap memory errors (even with export ES_HEAP_SIZE=4g) so decided to write a bash script to break the JSON file up before sending the JSON information to Elasticsearch, see the

elasticsearch-dump/elasticsearch-dump: Import and export , The reference file is located in the same directory as the metricbeat.yml file. Each file which is # monitored must contain one or multiple modules as a list. The filesystem metricset will not # collect data from filesystems matching any of the enabled: true # Apache hosts hosts: ["http://127.0.0.1"] # Path to server status. To import a JSON file into Elasticsearch, we will use the elasticdump package. It is a set of import and export tools used for Elasticsearch. This website uses cookies and other tracking technology to analyse traffic, personalise ads and learn how we can improve the experience for our visitors and customers.

elastic/elasticsearch-php: Official PHP low-level client for , Import and export tools for elasticsearch. Version 1.0.0 of Elasticdump changes the format of the files created by the dump. alias.json \ --output=http://es.com: 9200 \ --type=alias # Backup templates to a file elasticdump (default: 100) -- size How many objects to retrieve (default: -1 -> no limit) --concurrency The maximum� In this tip, I will load sample JSON files into SQL Server. Solution. Following are a couple examples of how to load JSON files into SQL Server. Importing simple JSON file into SQL Server . In this example, the data file contains the order details such as "OrderID", "CustomerID" and "OrderStatus" for 2 orders.

Comments
  • i know about bulk api but i do not want to use bulk becuause it requires manually editing of fields and schemas.i would like to upload json file in one shot. i used bulk-api but it requires manually editing.i would like to import my json as it is. anyway thanks for reply. i got stream2es (for stream input) and FSRiver for some extent these are usefull for me ------------------------------------------------------------------------
  • header line?..can you please explain that part?
  • This is a header { "index" : { "_index" : "test", "_type" : "type1", "_id" : "1" } }
  • It's not. And if you are using index/type/_bulk endpoint, you can also ignore _index and _type.
  • If we've removed _id, _index, and _type, then the object will be empty, like this: { "index" : { } } . Is that OK?
  • Just for information (in case anybody comes across this question), yes, it works with an empty index header, as written by @The Red Pea
  • This answer was extremely helpful. I was able to figure out how to get this working based on your explanation alone - if I could vote this twice, I would!
  • Thanks for the tip on using --data-binary - answered my question perfectly.
  • So unfortunate that ElasticSearch doesn't provide first-class support for huge JSON file import OOTB (jq is not feasible for Windows users and its kinda hacky).
  • There's something I don't get here. Won't this make pubs.length + forms.length different operations? Instead of just one, which is _bulk's point? I found this thread, where @keety's answer uses client.bulk() to insert everything in one operation, which makes way more sense IMO
  • @JeremyThille indeed that is the better way and at the time I wrote this I either hadn't made my way that far in the docs or it wasn't an option yet, and this worked for my very specific use-case. Now I don't use the JS client at all and do a direct call to /_bulk with all the data combined.
  • Could not thank you more for this. This is an awesome answer
  • This is the simplest approach