Import CSV Data

We’re overhauling Dgraph’s docs to make them clearer and more approachable. If you notice any issues during this transition or have suggestions, please let us know.

Convert CSV to JSON

There are many tools available to convert CSV to JSON. You can import large data sets to Dgraph using Dgraph Live Loader or Dgraph Bulk Loader. In these examples, the csv2json tool is used, and the data is imported using the Mutate tab in Ratel.

Before you begin

• Install csv2json conversion tool.
• Install jq a lightweight and flexible command-line JSON processor.
• Connect the Dgraph instance to Ratel for queries, mutations and visualizations.

Example 1

1. Create a names.csv file with these details:

   Name,URL
   Dgraph,https://github.com/hypermodeinc/dgraph
   Badger,https://github.com/hypermodeinc/badger
   

2. Change to the directory that contains the names.csv file and convert it to names.json:

   csv2json names.csv --out names.json
   

3. To prettify a JSON file, use the jq '.' command:

   cat names.json | jq '.'
   

   The output is similar to:

   [
     {
       "Name": "Dgraph",
       "URL": "https://github.com/hypermodeinc/dgraph"
     },
     {
       "Name": "Badger",
       "URL": "https://github.com/hypermodeinc/badger"
     }
   ]
   

   This JSON file follows the JSON Mutation Format, it can be loaded into Dgraph using Dgraph Live Loader , Dgraph Bulk Loader or the programmatic clients.

4. To load the data to Ratel and HTTP clients. The JSON data has to be stored within the "set" key. You can use jq to transform the JSON into the correct format:

   cat names.json | jq '{ set: . }'
   

   An output similar to this appears:

   {
     "set": [
       {
         "Name": "Dgraph",
         "URL": "https://github.com/hypermodeinc/dgraph"
       },
       {
         "Name": "Badger",
         "URL": "https://github.com/hypermodeinc/badger"
       }
     ]
   }
   

5. Paste the output in the Mutate tab of Console in Ratel.

6. Click Run to import data.

7. To view the imported data paste the following in the Query tab and click Run:

   {
    names(func: has(URL)) {
    Name
    }
   }
   

Example 2

1. Create a connects.csv file that’s connecting nodes together. The connects field should be of the uid type.

   uid,connects
   _:a,_:b
   _:a,_:c
   _:c,_:d
   _:d,_:a
   

2. To get the correct JSON format, you can convert the CSV into JSON and use jq to transform it in the correct format where the connects edge is a node uid. This JSON file can be loaded into Dgraph using the programmatic clients.

   csv2json connects.csv | jq '[ .[] | { uid: .uid, connects: { uid: .connects } } ]'
   

   The output is similar to:

   [
     {
       "uid": "_:a",
       "connects": {
         "uid": "_:b"
       }
     },
     {
       "uid": "_:a",
       "connects": {
         "uid": "_:c"
       }
     },
     {
       "uid": "_:c",
       "connects": {
         "uid": "_:d"
       }
     },
     {
       "uid": "_:d",
       "connects": {
         "uid": "_:a"
       }
     }
   ]
   

3. To get an output of the mutation format accepted in Ratel UI and HTTP clients:

   csv2json connects.csv | jq '{ set: [ .[] | {uid: .uid, connects: { uid: .connects } } ] }'
   

   The output is similar to:

   {
     "set": [
       {
         "uid": "_:a",
         "connects": {
           "uid": "_:b"
         }
       },
       {
         "uid": "_:a",
         "connects": {
           "uid": "_:c"
         }
       },
       {
         "uid": "_:c",
         "connects": {
           "uid": "_:d"
         }
       },
       {
         "uid": "_:d",
         "connects": {
           "uid": "_:a"
         }
       }
     ]
   }
   

   To reuse existing integer IDs from a CSV file as UIDs in Dgraph, use Dgraph Zero’s assign endpoint before loading data to allocate a range of UIDs that can be safely assigned.

4. Paste the output in the Mutate tab of Console in Ratel, and click Run to import data.

Assistant

Responses are generated using AI and may contain mistakes.