Struct LogsCompute

#[non_exhaustive]
pub struct LogsCompute {
    pub aggregation: LogsAggregationFunction,
    pub interval: Option<String>,
    pub metric: Option<String>,
    pub type_: Option<LogsComputeType>,
    pub additional_properties: BTreeMap<String, Value>,
    /* private fields */
}

A compute rule to compute metrics or timeseries

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

aggregation: LogsAggregationFunction

An aggregation function

interval: Option<String>

The time buckets’ size (only used for type=timeseries) Defaults to a resolution of 150 points

metric: Option<String>

The metric to use

type_: Option<LogsComputeType>

The type of compute

additional_properties: BTreeMap<String, Value>

Implementations

impl LogsCompute

pub fn new(aggregation: LogsAggregationFunction) -> LogsCompute

Examples found in repository

examples/v2_logs_AggregateLogs_2527007002.rs (line 13)

async fn main() {
    let body = LogsAggregateRequest::new()
        .compute(vec![LogsCompute::new(LogsAggregationFunction::COUNT)
            .interval("5m".to_string())
            .type_(LogsComputeType::TIMESERIES)])
        .filter(
            LogsQueryFilter::new()
                .from("now-15m".to_string())
                .indexes(vec!["main".to_string()])
                .query("*".to_string())
                .to("now".to_string()),
        );
    let configuration = datadog::Configuration::new();
    let api = LogsAPI::with_config(configuration);
    let resp = api.aggregate_logs(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

examples/v2_logs_AggregateLogs_2955613758.rs (line 18)

async fn main() {
    let body = LogsAggregateRequest::new()
        .compute(vec![LogsCompute::new(LogsAggregationFunction::COUNT)
            .interval("5m".to_string())
            .type_(LogsComputeType::TIMESERIES)])
        .filter(
            LogsQueryFilter::new()
                .from("now-15m".to_string())
                .indexes(vec!["main".to_string()])
                .query("*".to_string())
                .to("now".to_string()),
        )
        .group_by(vec![LogsGroupBy::new("host".to_string())
            .missing(LogsGroupByMissing::LogsGroupByMissingString(
                "miss".to_string(),
            ))
            .sort(
                LogsAggregateSort::new()
                    .aggregation(LogsAggregationFunction::PERCENTILE_90)
                    .metric("@duration".to_string())
                    .order(LogsSortOrder::ASCENDING)
                    .type_(LogsAggregateSortType::MEASURE),
            )]);
    let configuration = datadog::Configuration::new();
    let api = LogsAPI::with_config(configuration);
    let resp = api.aggregate_logs(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

pub fn interval(self, value: String) -> Self

Examples found in repository

examples/v2_logs_AggregateLogs_2527007002.rs (line 14)

async fn main() {
    let body = LogsAggregateRequest::new()
        .compute(vec![LogsCompute::new(LogsAggregationFunction::COUNT)
            .interval("5m".to_string())
            .type_(LogsComputeType::TIMESERIES)])
        .filter(
            LogsQueryFilter::new()
                .from("now-15m".to_string())
                .indexes(vec!["main".to_string()])
                .query("*".to_string())
                .to("now".to_string()),
        );
    let configuration = datadog::Configuration::new();
    let api = LogsAPI::with_config(configuration);
    let resp = api.aggregate_logs(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

examples/v2_logs_AggregateLogs_2955613758.rs (line 19)

async fn main() {
    let body = LogsAggregateRequest::new()
        .compute(vec![LogsCompute::new(LogsAggregationFunction::COUNT)
            .interval("5m".to_string())
            .type_(LogsComputeType::TIMESERIES)])
        .filter(
            LogsQueryFilter::new()
                .from("now-15m".to_string())
                .indexes(vec!["main".to_string()])
                .query("*".to_string())
                .to("now".to_string()),
        )
        .group_by(vec![LogsGroupBy::new("host".to_string())
            .missing(LogsGroupByMissing::LogsGroupByMissingString(
                "miss".to_string(),
            ))
            .sort(
                LogsAggregateSort::new()
                    .aggregation(LogsAggregationFunction::PERCENTILE_90)
                    .metric("@duration".to_string())
                    .order(LogsSortOrder::ASCENDING)
                    .type_(LogsAggregateSortType::MEASURE),
            )]);
    let configuration = datadog::Configuration::new();
    let api = LogsAPI::with_config(configuration);
    let resp = api.aggregate_logs(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

pub fn metric(self, value: String) -> Self

pub fn type_(self, value: LogsComputeType) -> Self

Examples found in repository

examples/v2_logs_AggregateLogs_2527007002.rs (line 15)

async fn main() {
    let body = LogsAggregateRequest::new()
        .compute(vec![LogsCompute::new(LogsAggregationFunction::COUNT)
            .interval("5m".to_string())
            .type_(LogsComputeType::TIMESERIES)])
        .filter(
            LogsQueryFilter::new()
                .from("now-15m".to_string())
                .indexes(vec!["main".to_string()])
                .query("*".to_string())
                .to("now".to_string()),
        );
    let configuration = datadog::Configuration::new();
    let api = LogsAPI::with_config(configuration);
    let resp = api.aggregate_logs(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

examples/v2_logs_AggregateLogs_2955613758.rs (line 20)

async fn main() {
    let body = LogsAggregateRequest::new()
        .compute(vec![LogsCompute::new(LogsAggregationFunction::COUNT)
            .interval("5m".to_string())
            .type_(LogsComputeType::TIMESERIES)])
        .filter(
            LogsQueryFilter::new()
                .from("now-15m".to_string())
                .indexes(vec!["main".to_string()])
                .query("*".to_string())
                .to("now".to_string()),
        )
        .group_by(vec![LogsGroupBy::new("host".to_string())
            .missing(LogsGroupByMissing::LogsGroupByMissingString(
                "miss".to_string(),
            ))
            .sort(
                LogsAggregateSort::new()
                    .aggregation(LogsAggregationFunction::PERCENTILE_90)
                    .metric("@duration".to_string())
                    .order(LogsSortOrder::ASCENDING)
                    .type_(LogsAggregateSortType::MEASURE),
            )]);
    let configuration = datadog::Configuration::new();
    let api = LogsAPI::with_config(configuration);
    let resp = api.aggregate_logs(body).await;
    if let Ok(value) = resp {
        println!("{:#?}", value);
    } else {
        println!("{:#?}", resp.unwrap_err());
    }
}

pub fn additional_properties(self, value: BTreeMap<String, Value>) -> Self

Trait Implementations

impl Clone for LogsCompute

fn clone(&self) -> LogsCompute

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for LogsCompute

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for LogsCompute

fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>

where D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for LogsCompute

fn eq(&self, other: &LogsCompute) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for LogsCompute

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for LogsCompute