Loading Data to a Google BigQuery Data Warehouse

The Pipeline loads the Source data every five minutes by default to your Google Cloud Storage (GCS) buckets, from where it is loaded to the BigQuery Destination.

This page describes the queries that Hevo runs in your Google BigQuery data warehouse for loading data into the Destination tables. Hevo uses primary keys, if any, for deduplicating the data before loading it to the target tables. In the absence of primary keys, Hevo appends data to the target tables. Read Loading Data to a Data Warehouse for a comprehensive understanding of Hevo’s data loading process.

Note: The queries listed here have been simplified to facilitate understanding the overall data deduplication and loading process.

Loading Data without Primary Keys

If primary keys are not present in the Destination tables, Hevo directly appends the data into the target tables in the Destination data warehouse using the following steps:

Apply the __hevo_ingested_at timestamp to each Event at the time of ingestion from Source. This column is retained in the Destination table.
Ensure __hevo_ingested_at and, if required, __hevo__loaded_at columns are present in the Destination table; else, create them.
Copy files into the target table:

   load <gs://path_to/file> to <dataset>.<target_table>

Loading Data with Primary Keys

If the Destination tables provide for primary keys, Hevo performs the following steps to deduplicate and load the data to the data warehouse.

Apply the __hevo_ingested_at timestamp to each Event at the time of ingestion from Source. This column is retained in the Destination table.
Ensure __hevo_ingested_at, and if required, __hevo__loaded_at columns are present in the Destination table; else, create them.
Create a temporary staging table with the same schema as the Destination table and the additional metadata columns. The ingested data is loaded to this table, and all the steps for deduplicating it are performed on this table. Read Creating and using tables in Google BigQuery.

Copy files to the staging table:

 load <gs://path_to/file> to <dataset>.<stage_table>

Check if the staging table contains any entries for deleted Events:

  SELECT COUNT(*) AS deleted_count
  FROM
    <dataset>.<stage_table>
  WHERE
    __hevo__marked_deleted = TRUE;

If the value of deleted_count obtained from Step 5 is greater than 0, perform this step. Else, skip to Step 7. In this step:

Create a temporary table (for example, delete_table) with the most recent deleted Events from the staging table:

CREATE TABLE
  <dataset>.<delete_table> AS
SELECT
  row.<PK1>,
  row.<PK2>,
  ...
  row.<PKn>,
  row.__hevo__ingested_at,
  row.__he__msg_seq_id,
  row.__hevo__consumption_id
  FROM (
    SELECT
    ARRAY_AGG (T
    ORDER BY
      __hevo__ingested_at DESC, __he__msg_seq_id DESC, __hevo__consumption_id DESC
      LIMIT 1)
      [OFFSET (0)] AS ROW
  FROM
    <dataset>.<stage_table> AS T
  WHERE
    __hevo__marked_deleted = TRUE
  GROUP BY <PK1, PK2,...,PKn>);  

Remove the entries for all deleted Events from the staging table:

DELETE
FROM
  <dataset>.<stage_table>
WHERE
  __hevo__marked_deleted = TRUE;

Delete duplicate data from the staging table based on __hevo__ingested_at, __he__msg_seq_id, and __hevo__consumption_id:

DELETE
FROM
  <dataset>.<stage_table>
WHERE
  STRUCT(<PK1, PK2, ..., PKn>, __hevo__ingested_at)
    NOT IN (SELECT AS STRUCT <PK1, PK2, ..., PKn>, MAX(__hevo__ingested_at)
            FROM
              <dataset>.<stage_table>
            GROUP BY <PK1, PK2, ..., PKn>);

DELETE
FROM
  <dataset>.<stage_table>
WHERE
  STRUCT(<PK1, PK2, ..., PKn>, __hevo__ingested_at)
    NOT IN (SELECT AS STRUCT <PK1, PK2, ..., PKn>, MAX(__he__msg_seq_id)
            FROM
              <dataset>.<stage_table>
            GROUP BY <PK1, PK2, ..., PKn>);

DELETE
FROM
  <dataset>.<stage_table>
WHERE
  STRUCT(<PK1, PK2, ..., PKn>, __hevo__ingested_at)
    NOT IN (SELECT AS STRUCT <PK1, PK2, ..., PKn>, MAX(__hevo__consumption_id)
            FROM
              <dataset>.<stage_table>
            GROUP BY <PK1, PK2, ..., PKn>);

If the delete_table was created in Step 6, perform this step. Else, skip to step 7. In this step:

Load data from the delete_table into the staging table.

Note: Google BigQuery uses a single Merge statement to handle the deletes, inserts, and updates to the staging table.

MERGE INTO
  <dataset>.<stage_table> S
USING
  <dataset>.<delete_table> D
ON
  S.PK1 = D.PK1 AND
  S.PK2 = D.PK2 AND
  ...
  S.PKn = D.PKn
WHEN MATCHED AND
    ((D.__hevo__ingested_at > S.__hevo__ingested_at) OR
     (D.__hevo__ingested_at = S.__hevo__ingested_at AND
      D.__he__msg_seq_id > S.__he__msg_seq_id) OR
     (D.__hevo__ingested_at = S.__hevo__ingested_at AND
      D.__he__msg_seq_id = S.__he__msg_seq_id AND
      D.__hevo__consumption_id > S.__hevo__consumption_id)) OR
    S.__hevo__ingested_at IS NULL
THEN UPDATE SET S.__hevo__is_merged = TRUE,
                S.__hevo__marked_deleted = TRUE,
                S.__hevo__ingested_at = D.__hevo__ingested_at
WHEN NOT MATCHED
  THEN INSERT (<PK1>,
               <PK2>,
                ...
               <PKn>,
               __hevo__consumption_id,
               __he__msg_seq_id,
               __hevo__ingested_at,
               __hevo__marked_deleted)
       VALUES (D.<PK1>,
               D.<PK2>,
               ...
               D.<PKn>,
               D.__hevo__consumption_id,
               D.__he__msg_seq_id,
               D.__hevo__ingested_at,
               D.__hevo__marked_deleted);

Drop the temporary delete_table.

Load the data from the staging table into the Destination table:

Note: Google BigQuery uses a single Merge statement to handle the deletes, inserts, and updates to the Destination table.

MERGE INTO
  <dataset>.<destination_table> AS T
USING
  <dataset>.<stage_table> AS S
ON
  T.PK1 = S.PK1 AND
  T.PK2 = S.PK2 AND
  ...
  T.PKn = S.PKn
WHEN MATCHED AND
    (S.__hevo__ingested_at >= T.__hevo__ingested_at OR T.__hevo__ingested_at IS NULL) AND
    (S.__hevo__marked_deleted IS NOT NULL AND S.__hevo__marked_deleted = TRUE) AND
    (S.__hevo__is_merged IS NULL)
    THEN UPDATE SET
        T.__hevo__marked_deleted = TRUE,
        T.__hevo__ingested_at = S.__hevo__ingested_at,
        T.__hevo__loaded_at = <loaded_at>
    WHEN MATCHED AND
        (S.__hevo__ingested_at >= T.__hevo__ingested_at OR T.__hevo__ingested_at IS NULL) AND
        (((S.__hevo__marked_deleted IS NOT NULL AND S.__hevo__marked_deleted = TRUE) AND
        (S.__hevo__is_merged = TRUE)) OR
        (S.__hevo__marked_deleted IS NULL OR S.__hevo__marked_deleted = FALSE))
        THEN UPDATE SET
            T.agent_name = S.agent_name,
            T.__hevo__ingested_at = S.__hevo__ingested_at,
            T.__hevo__marked_deleted = S.__hevo__marked_deleted,
            T.__hevo__loaded_at = <loaded_at>
    WHEN NOT MATCHED
        THEN INSERT (<PK1>,
                     <PK2>,
                     ...
                     <PKn>,
                     field_1,
                     field_2,
                     ...
                     field_n,
                     __hevo__ingested_at,
                     __hevo__marked_deleted,
                     __hevo__loaded_at)
        VALUES (S.<PK1>,
               S.<PK2>,
               ...
               S.<PKn>,
               S.field_1,
               S.field_2,
               ...
               S.field_n,
               S.__hevo__ingested_at,
               S.__hevo__marked_deleted,
               <loaded_at>);

Drop the staging table.

This completes the loading of data to the Google BigQuery data warehouse.

Loading Data to a Google BigQuery Data Warehouse

Loading Data without Primary Keys

Loading Data with Primary Keys

See Also

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