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See here for the full documentation.

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Examples

Snuba SDK is a tool that allows requests to Snuba to be built programatically. A Request consists of a Query, the dataset the Query is targeting, the AppID of the Request, and any flags for the Request. A Query object is a code representation of a SnQL query, and has a number of attributes corresponding to different parts of the query.

Requests and Queries can be created directly:

request = Request(
    dataset = "discover",
    app_id = "myappid",
    tenant_ids = {"referrer": "my_referrer", "organization_id": 1234}
    query = Query(
        match=Entity("events"),
        select=[
            Column("title"),
            Function("uniq", [Column("event_id")], "uniq_events"),
        ],
        groupby=[Column("title")],
        where=[
            Condition(Column("timestamp"), Op.GT, datetime.datetime(2021, 1, 1)),
            Condition(Column("project_id"), Op.IN, Function("tuple", [1, 2, 3])),
        ],
        limit=Limit(10),
        offset=Offset(0),
        granularity=Granularity(3600),
    ),
    flags = Flags(debug=True)
)

Queries can also be built incrementally:

query = (
    Query("discover", Entity("events"))
    .set_select(
        [Column("title"), Function("uniq", [Column("event_id")], "uniq_events")]
    )
    .set_groupby([Column("title")])
    .set_where(
        [
            Condition(Column("timestamp"), Op.GT, datetime.datetime.(2021, 1, 1)),
            Condition(Column("project_id"), Op.IN, Function("tuple", [1, 2, 3])),
        ]
    )
    .set_limit(10)
    .set_offset(0)
    .set_granularity(3600)
)

MQL Examples

MQL queries can be built in a similar way to SnQL queries. However they use a MetricsQuery object instead of a Query object. The query argument of a MetricsQuery is either a Timeseries or Formula, which is a mathemtical formula of Timeseries.

The other arguments to the MetricsQuery are meta data about how to run the query, e.g. start/end timestamps, the granularity, limits etc.

    MetricsQuery(
        query=Formula(
            ArithmeticOperator.DIVIDE.value,
            [
                Timeseries(
                    metric=Metric(
                        public_name="transaction.duration",
                    ),
                    aggregate="sum",
                ),
                1000,
            ],
        ),
        start=NOW,
        end=NOW + timedelta(days=14),
        rollup=Rollup(interval=3600, totals=None, granularity=3600),
        scope=MetricsScope(
            org_ids=[1], project_ids=[11], use_case_id="transactions"
        ),
        limit=Limit(100),
        offset=Offset(5),
    )

Once the request is built, it can be translated into a Snuba request that can be sent to Snuba.

# Outputs a formatted Snuba request
request.serialize()

It can also be printed in a more human readable format.

# Outputs a formatted Snuba request
print(request.print())

This outputs:

{
    "dataset": "discover",
    "app_id": "myappid",
    "query": "MATCH (events) SELECT title, uniq(event_id) AS uniq_events BY title WHERE timestamp > toDateTime('2021-01-01T00:00:00.000000') AND project_id IN tuple(1, 2, 3) LIMIT 10 OFFSET 0 GRANULARITY 3600",
    "debug": true
}

If an expression in the query is invalid (e.g. Column(1)) then an InvalidExpressionError exception will be thrown. If there is a problem with a query, it will throw an InvalidQueryError exception when .validate() or .translate() is called. If there is a problem with the Request or the Flags, an InvalidRequestError or InvalidFlagError will be thrown respectively.

Contributing to the SDK

Please refer to CONTRIBUTING.rst.

License

Licensed under FSL-1.0-Apache-2.0, see LICENSE.