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Snap Type:Write
Description:

This Snap executes a bulk load of the incoming documents into the Google BigQuery database directly without any intermediary such as Google Cloud Storage. The data is loaded in batches of configurable size. Once a batch is successfully loaded into the target table, the next batch is loaded. The batch load is also subject to a timeout. If the timeout is reached before all the documents in the batch are loaded, the batch is flushed and the next batch's bulk load is started. If the bulk load fails, then the Snap will try to load the batch again after a time delay. Retry occurs for a set number of times. The batch timeout limit, size of a batch, the time delay between retries, and the number of retries are all configurable.

ETL Transformations & Data Flow

Data from the incoming documents is streamed in batches to the destination table. The user can configure Project ID, Dataset ID, and Table ID of the destination. In addition, if the table into which the data is to be loaded does not exist, the Snap can create the table using the configured Table ID.

Input & Output

  • Input: Any Snap that can pass a document output view, such as Structure or JSON Generator. Pipeline parameters can also be passed for project ID, dataset ID, and table ID, and so on.
  • Output: The output is in document view format. The data from the incoming document is loaded to the destination table is the output from this Snap. It gives the load statistics after the operation is completed.

Modes

Note
titleSnaps in Google BigQuery Snap Pack

  • Write datetime values to the database tables, always in UTC format.

  • Convert any non-UTC values in the incoming data to UTC before writing them.

  • Consider datetime values without the time zone mentioned, as in UTC.

So, ensure that you include the time zone in all the datetime values that you load into Google BigQuery tables using this Snap.

For example: "2020-08-29T18:38:07.370 America/Los_Angeles", “2020-09-11T10:05:14.000-07:00", “2020-09-11T17:05:14.000Z”


Prerequisites:

Write access to the Google BigQuery Account is required.

Limitations and Known Issues

The batch size can be a maximum of 10,000.

Known Issue

If you copy

Copying data by creating a table with the same

table

name in Google BigQuery immediately after

immediately

deleting it, may

cause a loss of data

not insert the rows as expected. This behavior is

expected

due to the way the tables are cached and the internal table ID is propagated throughout the system.

It is recommended to avoid rewriting in Big Query.

Workaround

Truncate the

 

Workaround

We recommend you avoid rewriting in Google BigQuery and suggest the following workarounds. You can choose to use them individually or in unison, to suit your requirement.

  • Truncate the existing table instead of deleting it.

  • Add some randomly generated prefix to the table name each time you create the table

, so it has a new

  • (new or unique table name every time).

Configurations:

Account & Access

This Snap uses account references created on the Accounts page of SnapLogic Manager to handle access to this endpoint. See Google BigQuery Account for information on setting up this type of account.

Views

InputThis Snap has exactly one document input view.
OutputThis Snap has at most one document output view.
ErrorThis Snap has at most one document error view and produces zero or more documents in the view.


Troubleshooting:[None]

Settings

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Project ID

Required. Project ID of the project billed for the query. This is a suggestible field and can be populated based on the Account settings.

Example: case1234

Default value: [None]

Dataset ID

Required. Dataset ID of the destination table. This is a suggestible field and all the datasets in the specified project will be populated.

Example: babynames

Default value: [None]

Table ID

Required. Table ID of the destination table. This is a suggestible field and all the tables in the datasets will be listed.

Example: customerdata1

Default value: [None]

Create table if not present

Specifies that the table should be created if not already present in the database.

Default value: Not selected

Batch size

The number of records batched per request. If the input has 10,000 records , and the batch size is set to 100, the total number of requests would be 100.

Default value: 1000

Batch timeout (milliseconds)

Time in milliseconds to elapse following which the batch if not empty will be processed even though it might be lesser than the given batch size.

Default value: 2000

Note

Batch timeout value must be set with care. When this limit is reached, the batch will be flushed irrespective of whether all the records in the batch were loaded.


Batch retry count

The number of times the server should try to load a failed batch.

Default value: 0

Batch retry delay (milliseconds)

Time The time delay between each retry.

Default value: 500

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Note
titleWriting numeric values into Google BigQuery tables

Google BigQuery tables support columns with NUMERIC with a NUMERIC data type to allow storing big decimal numbers (up to 38 digits with nine decimal places). But Snaps in Google BigQuery Snap Pack that load data into tables cannot create numeric columns. When the Create table if not present check  check box is selected, the Snaps create the required table schema, but map big decimals to a FLOAT64 column. So, to store the data into numeric columns using these Snaps, we recommend the following actions:

  • Create the required schema, beforehand, with numeric columns in Google BigQuery.
  • Pass the number as a string.

The Google API converts this string into a number with full precision and saves it in the numeric column.

Example: 

Value Passed Through SnapValue Stored in BigQueryRemarks
"12345678901234567890123456789.123456789"12345678901234567890123456789.123456789As per this issue logged in Google Issue Tracker, if you send the values as strings, the values are never converted to floating-point form, so this works as expected.
12345678901234567890123456789.123456789123456789012345678000000000000Big decimal values sent as non-string values lose precision.


Examples

Basic Use Case

The following pipeline describes how the Snap functions as a standalone Snap in a pipeline:

In this example, the Google BigQuery Bulk Load (Streaming) Snap is used to load records from the table snapteam1.postgretimetypes into the target table Atable1 in the babynames dataset within the case16370 project. The input records are provided through the PostgreSQL Select Snap. The input records from the upstream PostgreSQL Select Snap are shown below:

The Google BigQuery Bulk Load (Streaming) Snap's configuration is as shown below:


The following is a preview of the output from the Snap:


The exported pipeline is available in the Downloads section below.

Typical Snap Configurations

The key configuration of the Snap lies in how the values are passed. There are two ways of passing values:

  • Without expressions: The values are passed in the Snap directly.

  • With expressions
    • Using Pipeline parameters: Values are passed in the Snap as pipeline parameters. Select the parameters to be applied by enabling the corresponding checkbox under the Capture column.


 

Advanced Use Case

The following describes a pipeline with a broader business logic involving multiple ETL transformations. It shows how, in an enterprise environment, Bulk Load functionality can typically be used. 

This pipeline moves records from a SQL Server instance to the Google BigQuery instance, that which is on-premise data migration to the cloud environment.

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  1. Extract: Records are extracted from the SQL Server instance by the SQL Server - Select Snap.
  2. Load: The extracted records from the SQL Server Snap are loaded by the Google BigQuery Bulk Load (Streaming) Snap into the destination table.
  3. Extract: The Google BigQuery Execute Snap extracts the records inserted into the destination table by the Google BigQuery Bulk Load (Streaming) Snap.

The exported pipeline is available in the Downloads section below.

Downloads

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