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google-cloud-bigquery
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Node.js package to create BigQuery table from Google Cloud Storage or load data into Google Cloud BigQuery tables including automatically updating the tables' schema.
Google Cloud BigQuery is node.js package to create BigQuery table from Google Cloud Storage or load data into Google Cloud BigQuery tables including automatically updating the tables' schema.
npm i google-cloud-bigquery --save
Before using this package, you must first:
Have a Google Cloud Account.
Have a both a BigQuery DB and a Bucket in the same region.
Have a Service Account set up with the following 2 roles:
roles/storage.objectAdmin
roles/bigquery.admin
Get the JSON keys file for that Service Account above
Save that JSON key into a service-account.json
file. Make sure it is located under a path that is accessible to your app (the root folder usually).
const { join } = require('path')
const { client } = require('google-cloud-bigquery')
const bigQuery = client.new({ jsonKeyFile: join(__dirname, './serviceaccount.json') })
const YOUR_DB = 'your-dataset-id'
// Assumes that YOUR_DB already exists
const db = bigQuery.db.get(YOUR_DB)
// Example 1 - Creating a new table, adding data, reading data
const YOUR_TABLE = 'user'
db.table(YOUR_TABLE).exists()
.then(yes => {
if (yes)
console.log(`Table '${YOUR_TABLE}' already exists in DB '${YOUR_DB}'`)
else
return db.table(YOUR_TABLE).create.new({
schema: {
id: 'integer',
username: 'string',
friends: [{
id: 'integer',
username: 'string',
score: 'float'
}],
country: {
code: 'string',
name: 'string'
},
married: 'boolean',
tags:['string'],
inserted_date: 'timestamp'
}
})
})
.then(() => db.table(YOUR_TABLE).insert.values({ data:[{
id: 1,
username: 'Nicolas',
inserted_date: new Date()
}, {
id: 2,
username: 'Brendan',
country: {
code: 'AU',
name: 'Australia'
},
friends:[{
id: 1,
username: 'Nicolas',
score: 0.87
}, {
id: 3,
username: 'Boris',
score: 0.9
}],
inserted_date: new Date()
}, {
id: '3',
username: 'Boris',
tags:['admin',1],
inserted_date: Date.now()/1000
}]
}))
.then(() => db.query.execute({
sql:`select * from ${YOUR_DB}.${YOUR_TABLE} where id = @id`,
params: { id: 2 }
}))
.then(({ data }) => console.log(JSON.stringify(data, null, ' ')))
// Query Output
// ============
//
// [
// {
// "id": 2,
// "username": "Brendan",
// "friends": [
// {
// "id": 1,
// "username": "Nicolas",
// "score": 0.87
// },
// {
// "id": 3,
// "username": "Boris",
// "score": 0.9
// }
// ],
// "country": {
// "code": "AU",
// "name": "Australia"
// },
// "married": null,
// "tags": [],
// "inserted_date": "2018-11-14T03:17:16.830Z"
// }
// ]
BigQuery casting capabilities are quite limited. When a type does not fit into the table, that row will either crashes the entire insert, or will be completely be ignored (we're using that last setting). To make sure that as much data is being inserted as possible, we've added an option called forcedSchema
in the db.table('some-table').insert.values
api:
db.table(YOUR_TABLE).insert.values({
data:{
id: '123.34',
username: { hello: 'world' },
inserted_date: new Date(2018,10,14)
},
forcedSchema:{
id: 'integer',
username: 'string',
inserted_date: 'timestamp'
}
})
Under the hood, this code will transform the data payload to the following:
{
id: 123,
username: 'Object',
inserted_date: '2018-11-13T13:00:00.000Z'
}
This object is guaranteed to comply to the schema so as much data is being inserted.
Notice the usage of the
bigQuery.job.get
to check the status of the job. The signature of that api is as follow:bigQuery.job.get({ projectId: 'your-project-id', location: 'asia-northeast1', jobId: 'a-job-id' })
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Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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FAQs
Node.js package to create BigQuery table from Google Cloud Storage or load data into Google Cloud BigQuery tables including automatically updating the tables' schema.
The npm package google-cloud-bigquery receives a total of 43 weekly downloads. As such, google-cloud-bigquery popularity was classified as not popular.
We found that google-cloud-bigquery demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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