GCP BigQuery Client

An ergonomic Rust async client library for GCP BigQuery. * Support all BigQuery API endpoints (not all covered by unit tests yet) * Support Service Account Key authentication, workload identity, installed flow and other yup-oauth2 mechanisms * Create tables and rows via builder patterns * Persist complex Rust structs in structured BigQuery tables * Async API * Support for JSON column types * Support serde::de::DeserializeOwned for get methods * Support BigQuery emulator

Features: - rust-tls (default): RUSTLS-based - native-tls: OpenSSL-based

Contributions are welcome.
Please post your suggestions and ideas on this GitHub discussion section.

Example

This example performs the following operations:

• Load a set of environment variables to set $PROJECT_ID, $DATASET_ID, $TABLE_ID and $GOOGLE_APPLICATION_CREDENTIALS
• Init the BigQuery client
• Create a dataset in the GCP project $PROJECT_ID
• Create a table in the previously created dataset (table schema)
• Insert a set of rows in the previously created table via the BigQuery Streaming API. The inserted rows are based on a regular Rust struct implementing the trait Serialize.
• Perform a select query on the previously created table
• Drop the table previously created
• Drop the dataset previously created

```rust // Init BigQuery client let client = gcpbigqueryclient::Client::fromserviceaccountkeyfile(gcpsakey).await?;

// Delete the dataset if needed
let result = client.dataset().delete(project_id, dataset_id, true).await;
if let Ok(_) = result {
    println!("Removed previous dataset '{}'", dataset_id);
}

// Create a new dataset
let dataset = client
    .dataset()
    .create(
        Dataset::new(project_id, dataset_id)
            .location("US")
            .friendly_name("Just a demo dataset")
            .label("owner", "me")
            .label("env", "prod"),
    )
    .await?;

// Create a new table
let table = dataset
    .create_table(
        &client,
        Table::from_dataset(
            &dataset,
            table_id,
            TableSchema::new(vec![
                TableFieldSchema::timestamp("ts"),
                TableFieldSchema::integer("int_value"),
                TableFieldSchema::float("float_value"),
                TableFieldSchema::bool("bool_value"),
                TableFieldSchema::string("string_value"),
                TableFieldSchema::record(
                    "record_value",
                    vec![
                        TableFieldSchema::integer("int_value"),
                        TableFieldSchema::string("string_value"),
                        TableFieldSchema::record(
                            "record_value",
                            vec![
                                TableFieldSchema::integer("int_value"),
                                TableFieldSchema::string("string_value"),
                            ],
                        ),
                    ],
                ),
            ]),
        )
        .friendly_name("Demo table")
        .description("A nice description for this table")
        .label("owner", "me")
        .label("env", "prod")
        .expiration_time(SystemTime::now() + Duration::from_secs(3600))
        .time_partitioning(
            TimePartitioning::per_day()
                .expiration_ms(Duration::from_secs(3600 * 24 * 7))
                .field("ts"),
        ),
    )
    .await?;
println!("Table created -> {:?}", table);

// Insert data via BigQuery Streaming API
let mut insert_request = TableDataInsertAllRequest::new();
insert_request.add_row(
    None,
    MyRow {
        ts: OffsetDateTime::now_utc(),
        int_value: 1,
        float_value: 1.0,
        bool_value: false,
        string_value: "first".into(),
        record_value: FirstRecordLevel {
            int_value: 10,
            string_value: "sub_level_1.1".into(),
            record_value: SecondRecordLevel {
                int_value: 20,
                string_value: "leaf".to_string(),
            },
        },
    },
)?;
insert_request.add_row(
    None,
    MyRow {
        ts: OffsetDateTime::now_utc(),
        int_value: 2,
        float_value: 2.0,
        bool_value: true,
        string_value: "second".into(),
        record_value: FirstRecordLevel {
            int_value: 11,
            string_value: "sub_level_1.2".into(),
            record_value: SecondRecordLevel {
                int_value: 21,
                string_value: "leaf".to_string(),
            },
        },
    },
)?;
insert_request.add_row(
    None,
    MyRow {
        ts: OffsetDateTime::now_utc(),
        int_value: 3,
        float_value: 3.0,
        bool_value: false,
        string_value: "third".into(),
        record_value: FirstRecordLevel {
            int_value: 12,
            string_value: "sub_level_1.3".into(),
            record_value: SecondRecordLevel {
                int_value: 22,
                string_value: "leaf".to_string(),
            },
        },
    },
)?;
insert_request.add_row(
    None,
    MyRow {
        ts: OffsetDateTime::now_utc(),
        int_value: 4,
        float_value: 4.0,
        bool_value: true,
        string_value: "fourth".into(),
        record_value: FirstRecordLevel {
            int_value: 13,
            string_value: "sub_level_1.4".into(),
            record_value: SecondRecordLevel {
                int_value: 23,
                string_value: "leaf".to_string(),
            },
        },
    },
)?;

client
    .tabledata()
    .insert_all(project_id, dataset_id, table_id, insert_request)
    .await?;

// Query
let mut rs = client
    .job()
    .query(
        project_id,
        QueryRequest::new(format!(
            "SELECT COUNT(*) AS c FROM `{}.{}.{}`",
            project_id, dataset_id, table_id
        )),
    )
    .await?;
while rs.next_row() {
    println!("Number of rows inserted: {}", rs.get_i64_by_name("c")?.unwrap());
}

// Delete the table previously created
client.table().delete(project_id, dataset_id, table_id).await?;

// Delete the dataset previously created
client.dataset().delete(project_id, dataset_id, true).await?;

```

An example of BigQuery load job can be found in the examples directory.

Status

The API of this crate is still subject to change up to version 1.0.

List of endpoints implemented: - [X] Dataset - All methods - [X] Table - All methods - [X] Tabledata - All methods - [X] Job - All methods - [X] Model - All methods (not tested) - [X] Project (not tested) - [X] Routine - All methods (not tested)

License

^{Licensed under either of Apache License, Version 2.0 or MIT license at your option.}

_{Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this crate by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.}