On this Page

Snap Type: Write

Description:

This Snap performs a bulk load operation into a Google BigQuery database. Depending upon the Snap's configuration, it does so either by using data from incoming documents or by using existing files in the Google Cloud Storage bucket. The Snap supports all three file types supported by Google BigQuery - CSV, JSON, and AVRO.

When using incoming documents:

In the case where data from incoming documents is being loaded, the data is first uploaded to a temporary file on Google Cloud Storage and from that temporary file the data is loaded into the destination table. The user can choose to either retain or delete this temporary file after the Snap terminates.

When using existing files from Google Cloud Storage:

In the case where existing files from Google Cloud Storage are being used, the data is loaded directly from the specified files into the destination table, there are no temporary files created for this operation. However, the user can choose to either retain or delete these existing files after the Snap terminates.

ETL Transformations & Data Flow

The Google BigQuery Bulk Load (Cloud Storage) Snap performs a bulk load of the input records into the specified database. If the data is being loaded from incoming documents, it is sent to a temporary file in the cloud storage and from there to the destination table. The temporary file is retained after Snap's execution. However, the user can choose to delete it if they so wish and configure the Snap accordingly.

Input & Output

Input: Any Snap that can pass a document output view, such as Structure or JSON Generator. Pipleline parameters can also be passed, only for bucket ID, project ID, table ID, and so on. In cases where existing files from the Google Cloud Storage bucket are being used, then input data is taken from the source files.
Output: The output is in document view format. It displays the statistics for the completed bulk load operation.

Modes

Ultra Pipelines: Works in Ultra Pipelines.

Consider the following points when using this Snap:

Ensure the schema of the incoming documents or existing files matches the schema of the destination table if CSV format is used. Refer to the Troubleshooting section for a workaround in this case.
Ensure when using existing files that the destination table exists.
Ensure when using incoming documents that the selected file type for the temporary file supports the data types in the incoming document. For example, CSV file format does not support arrays/lists, AVRO file format does not support DATE TIME data type, and so on.
The JSON format works for any data type and any complex schema.

Snaps 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 and Read & Write access from/to the Google Cloud Storage account is required.

Limitations and Known Issues

When using incoming documents with arrays/lists, CSV cannot be selected as the file format for the temporary file. This will cause an error. Select JSON or AVRO instead.
When using incoming documents with date time data types, AVRO cannot be selected as the file format for the temporary file. This will cause an error. Select CSV or JSON.
When uploading incoming documents via a CSV file or uploading an existing CSV file from Google Cloud Storage, make sure the CSV file contains all the destination table columns in the same order as per the table. Otherwise, use the workarounds in the Troubleshooting section to handle a CSV file that does not contain all the table columns in the same order.

When uploading from existing documents on Google Cloud Storage, enabling the Create table if not the present check box throws an exception error. To avoid this, ensure the destination table exists.

Known Issue

Copying data by creating a table with the same name in Google BigQuery immediately after deleting it, might not insert the rows as expected. This behavior is due to the way the tables are cached and the internal table ID is propagated throughout the system.

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 (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

Input	This Snap has exactly at most one document input view.
Output	This Snap has exactly one document output view.
Error	This Snap has at most one document error view and produces zero or more documents in the view.

Troubleshooting:

Mismatch in the order of columns/number of columns between incoming document/existing Google Cloud Storage files in CSV format:

Incoming documents: Use JSON/AVRO format for the temp file in the Cloud Storage Bulk Load Snap.
Existing files from Google Cloud Storage: Read the file using File Reader Snap followed by the CSV Parser Snap, then use Google BigQuery Bulk Load (Cloud Storage) Snap and selec Upload type as Upload incoming documents and File format as JSON or AVRO.

Settings

Label*

Specify a name for the Snap. You can modify this to be more specific, especially if you have more than one of the same Snap in your Pipeline.

Project ID*

Specify the Project ID of the project containing the table. This is a suggestible field and can be populated based on the Account settings.

Default value: None
Example: project1234

Dataset ID

Specify the dataset ID of the dataset containing the table. This is a suggestible field and all the datasets in the specified project will be populated.

Default value: None
Example: dataset1234

Table ID*

Specify the name of the target table into which the records are to be loaded. This is a suggestible field and all the tables in the datasets will be listed.

Default value: None
Example: table1234

Create table if not present

Select this checkbox to automatically create the target table if it does not exist.

If a second input view is configured for the Snap and it contains a document with schema (metadata) of the source table, the Snap creates the new (target) table using the same schema (metadata). However, if the schema comes from a different database, the Snap might fail with Unable to create table: "<table_name>" error due to data type incompatibility.
In the absence of a second input view (the schema/metadata document), the Snap creates a table based on the data types of the columns generated from the first row of the input document (first input view).

Due to implementation details, a newly created table is not visible to subsequent database Snaps during runtime validation. If you wish to immediately use the newly updated data you must use a child Pipeline that is invoked through a Pipeline Execute Snap.

Default value: Not selected

When using "Use existing files Upload type" option

Ensure that you do not select Create table if not present check box if you choose the Upload existing files on Google Cloud Storage option for Upload type, else an exception is thrown and the Snap does not execute.

Bucket name

Specify the name of the Google Cloud Storage bucket to be used for the operation. This is a suggestible field and will list all the buckets within the given account.

Default value: None
Example: project1234

Upload type

Choose an upload type for uploading your documents. The available options are:

Upload incoming documents
Upload existing files from Google Cloud Storage: To upload data from existing files in Google Cloud Storage bucket select the option Upload existing files from Google Cloud Storage.

These options specify the data source to the Snap. Incoming files or existing files in Google Cloud Storage bucket have to be uploaded.

Default value: Upload existing files from Google Cloud Storage
Example: Upload incoming documents

Based on the option you choose, only one of the succeeding sections has to be configured. The succeeding sections being Properties for uploading incoming documents and Properties for uploading existing files from Google Cloud Storage.

Properties for uploading incoming documents

This sub-section has to be configured if the option Upload incoming documents was selected in the Upload type property.

File format

Choose the file preferred format of the temporary file. The available options are: CSV, JSON, and AVRO.

Default value: CSV
Example: JSON

File formats and their limitations

The file format should be selected based on the data type they support, for example if the incoming document contains arrays or lists then selecting CSV in the File format property will throw an execption and the Snap will not execute. To avoid this, AVRO or JSON must be selected. Similarly, AVRO file format should not be used for Date Time data types.

If the incoming documents do not contain all the table columns in the same order as the destination table then do not use CSV.

Temp file name

The name of the temporary file that is created on the Google Cloud Storage bucket. If a filename is not provided, then a system generated file name is used.

Default value: [None]

Preserve temp file

Specify whether the temporary file created for the load operation has to be retained or deleted after the Snap's execution. By default the temporary file is deleted.

Default value: Selected

Properties for uploading existing files from Google Cloud Storage

This sub-section has to be configured if the option "Upload existing files from Google Cloud Storage" was selected in the Upload type property.

File paths Multiple files can be selected based on the need. When the pipeline is executed, the output data will have as many records listed. Based on the number of files added, the Snap will group them into categories (CSV with header & delimiter, CSV with delimiter but without header, JSON, and AVRO). This distinction is maintained in the output preview as well (shown distinctly according to File type).

File format

This is a drop-down list that has three options: CSV, JSON, and AVRO.

Default value: CSV

File path

The file's location in the Google Cloud Storage bucket.

Example: gs://gcs_existingbucket/exisitng_file.csv.

Default value: [None]

CSV file contains headers

Specifies that the CSV file contains headers. Use this option to enable the Snap to differentiate between the headers and records.

Default value: Not selected

CSV delimiter

Specify the delimiter for the CSV file. This is needed only for CSV file types.

Default value: , (comma)
Example: | (pipe)

Custom delimiters

All custom delimiters supported by BigQuery are supported as well.

Delete files upon exit

Similar operation as Preserve temp file. If this option is enabled, the files from which the data is loaded to the destination table are deleted after Snap's execution.

Default value: Not selected

Snap Execution

Select one of the three modes in which the Snap executes. Available options are:

Validate & Execute: Performs limited execution of the Snap, and generates a data preview during Pipeline validation. Subsequently, performs full execution of the Snap (unlimited records) during Pipeline runtime.
Execute only: Performs full execution of the Snap during Pipeline execution without generating preview data.
Disabled: Disables the Snap and all Snaps that are downstream from it.

Writing numeric values into Google BigQuery tables

Google BigQuery tables support columns with 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 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 Snap	Value Stored in BigQuery	Remarks
`"12345678901234567890123456789.123456789"`	12345678901234567890123456789.123456789	As 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.
`12345678901234567890123456789.123456789`	123456789012345678000000000000	Big decimal values sent as non-string values lose precision.

Examples

Basic Use Case - 1 (Upload incoming documents)

In this example, incoming documents are used for the bulk load. It will cover two scenarios:

Temporary file is preserved.
Temporary file is deleted.

The following pipeline is executed:

The CSV Generator Snap provides the input data to the Snap. A preview of the sample data from the incoming document is shown below:

Scenario - 1 - Temporary File Preserved

The following image is the configuration of the Google BigQuery Bulk Load (Cloud Storage) Snap:

The Preserve temp file checkbox is selected and the temprary file is specified as tempfile.csv. Upon execution, the records from the incoming CSV file are loaded into the temporary file present in the gcs_cloud_1 bucket. From there it is loaded into the destination table csvtablenew in the babynames dataset within the case16370 project.

The following is a preview of the data output from the Google BigQuery Bulk Load (Cloud Storage) Snap:

Scenario - 2 - Temporary File Deleted

The following image is the configuration of the Google BigQuery Bulk Load (Cloud Storage) Snap:

The Preserve temp file checkbox is not selected and the temprary file is specified as tempfile.csv. Upon execution, the records from the incoming CSV file are loaded into the temporary file present in the gcs_cloud_1 bucket. From there it is loaded into the destination table csvtablenew in the babynames dataset within the case16370 project. The temporary file is deleted.

The following is a preview of the data output from the Google BigQuery Bulk Load (Cloud Storage) Snap:

Basic Use Case - 2 (Upload Existing Files)

In this example, incoming documents are used for the bulk load. It will cover two scenarios:

Cloud Storage file is preserved.
Cloud Storage file is deleted.

The following pipeline is executed:

Scenario - 1 - Cloud Storage File Preserved

The following image is the configuration of the Google BigQuery Bulk Load (Cloud Storage) Snap:

An AVRO file is chosen as the input file and the Delete files upon exit checkbox is not selected. Upon the Snap's execution, the data from the input file is loaded into the table named avrotable in the babynames dataset within the case16370 project. The AVRO file is not deleted.

The following is a preview of the Snap's output:

Scenario - 2 - Cloud Storage File Deleted

The following image is the configuration of the Google BigQuery Bulk Load (Cloud Storage) Snap:

The following is a preview of the Snap's output:

The exported pipeline is available in Downloads section below.

Typical Snap Configurations

The key configuration of the Snap lies in how the values are passed to the Snap. This can be done in the following ways:

Without expressions
The values are passed to the Snap directly.
With expressions
- Using pipeline parameters
  The values are passed as pipeline parameters. The parameters to be applied have to be selected 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 reads social data and writes it into an existing file in the Google Cloud Storage. Bulk load of this social data is then performed from the existing file in the Google Cloud Storage to the destination table.

Extract: Social data matching a certain criteria is extracted by the Query Snap.
Transform: The extracted data from the Query Snap is transformed by the CSV Formatter Snap.
Load: The incoming data from the CSV Formatter Snap is written into a CSV file in Cloud Storage by the File Writer Snap.
Extract & Load: The Google BigQuery Bulk Load (Cloud Storage) Snap extracts the data from the existing CSV file in Cloud Storage and performs bulk load of this extracted data into the destination table.
Extract: The Google BigQuery Execute Snap extracts the data inserted into the destination table by the Google BigQuery Bulk Load (Cloud Storage) Snap.

The exported pipeline is available in the Downloads section below.

Downloads

	File	Modified
Labels No labels View	File Basic Use Case 1_Scenario_1_Google BigQuery Bulk Load GCS.slp	Oct 12, 2017 by Mohammed Iqbal
Labels No labels View	File Basic Use Case 1_Scenario_2_Google BigQuery Bulk Load GCS.slp	Oct 12, 2017 by Mohammed Iqbal
Labels No labels View	File Basic Use Case 2_Scenario_1_Google BigQuery Bulk Load GCS.slp	Oct 12, 2017 by Mohammed Iqbal
Labels No labels View	File Basic Use Case 2_Scenario_2_Google BigQuery Bulk Load GCS.slp	Oct 12, 2017 by Mohammed Iqbal
Labels No labels View	File Advanced Use Case_Google BigQuery Bulk Load GCS.slp	Nov 07, 2017 by Mohammed Iqbal

Download All

Snap Pack History

Click to view/expand

Release	Snap Pack Version	Date	Type	Updates
4.26	main11181	14 Aug 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.25	main9554	08 May 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.24	main8556	13 Feb 2021	Stable	Upgraded with the latest SnapLogic Platform release.
4.23	main7430	14 Nov 2020	Stable	Enhanced the Google BigQuery Execute Snap and Google BigQuery accounts to enable you to choose the SQL dialect to use in the Query field. See Interpreting the SQL Query Dialect for more information. Fixed the precision loss in Google BigQuery Execute Snap output that strips millisecond values while retrieving TIMESTAMP values from Google BigQuery tables.
4.22	main6403	12 Sep 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.21	snapsmrc542	09 May 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.20 Patch	google/bigquery8773	19 Mar 2020	Latest	Fixed the NPE issue with stored procedures and DROP TABLE queries in the Google BigQuery Execute Snap.
4.20	snapsmrc535	08 Feb 2020	Stable	Upgraded with the latest SnapLogic Platform release.
4.19	snaprsmrc528	14 Nov 2019	Stable	Upgraded with the latest SnapLogic Platform release.
4.18	snapsmrc523	10 Aug 2019	Stable	Upgraded with the latest SnapLogic Platform release.
4.17	ALL7402	11 Jun 2019	Latest	Pushed automatic rebuild of the latest version of each Snap Pack to SnapLogic UAT and Elastic servers.
4.17	snapsmrc515	11 Jun 2019	Stable	Added the Snap Execution field to all Standard-mode Snaps. In some Snaps, this field replaces the existing Execute during preview check box.
4.16	snapsmrc508	16 Feb 2019	Stable	Upgraded with the latest SnapLogic Platform release.
4.15	snapsmrc500	15 Dec 2018	Stable	Upgraded with the latest SnapLogic Platform release.
4.14	snapsmrc490	11 Aug 2018	Stable	Upgraded with the latest SnapLogic Platform release.
4.13	snapsmrc486	12 May 2018	Stable	Upgraded with the latest SnapLogic Platform release.
4.12	snapsmrc480	17 Feb 2018	Stable	Added a new property Schema auto detect in the Google BigQuery Bulk Load (Cloud Storage) Snap to support CSV and JSON files where one or more columns in the source file may not contain any values.
4.11	snapsmrc465	11 Nov 2017	Stable	Added new Snap: Google BigQuery Bulk Load (Cloud Storage) Added new Snap: Google BigQuery Bulk Load (Streaming). Updated Google Big Query Write Snap with a new Create table if not present property.
4.10 Patch	google/bigquery4046	05 Oct 2017	Latest	Addressed an issue when authenticating with Dynamic OAuth accounts.
4.10	snapsmrc414	12 Aug 2017	Stable	Upgraded with the latest SnapLogic Platform release.
4.9	snapsmrc405	13 May 2017	Stable	Upgraded with the latest SnapLogic Platform release.
4.8 Patch	bigquery2952	27 Apr 2017	Latest	Supports refreshing OAuth access tokens during long-running pipeline executions. Fixed an issue with writing small batch sizes and when querying empty dataset tables.
4.8.0 Patch	bigquery2813	05 Apr 2017	Latest	Reload OAuth account from Platform when the access token expires during pipeline execution.
4.8	snapsmrc398	11 Feb 2017	Stable	Upgraded with the latest SnapLogic Platform release.
4.7	snapsmrc382	23 Nov 2016	Stable	Upgraded with the latest SnapLogic Platform release.
4.6	snapsmrc362	13 Aug 2016	Stable	Upgraded with the latest SnapLogic Platform release.
4.5.1	snapsmrc344	18 May 2016	Stable	Upgraded with the latest SnapLogic Platform release.
4.4.1	NA	18 Mar 2016	Stable	Upgraded with the latest SnapLogic Platform release.
4.4	NA	13 Feb 2016	Stable	Upgraded with the latest SnapLogic Platform release.
4.3.2	NA	15 Jan 2016	Stable	Resolved the following issues with the Google BigQuery Execute Snap: throwing binary data in stacktrace and two error messages. improve error handling for suggestions improve error handling on bad queries suggestion bubble missing for Destination table ID Resolved an issue with Auto refresh token not working in Google BigQuery account.