Struct TensorBaseMetricsBackend 

pub struct TensorBaseMetricsBackend(/* private fields */);

Scale-out TensorBase metrics storage over its ClickHouse-compatible protocol.

Use connect with a full protocol URL or connect_host for the default native port (9528). Pair this type with TensorBaseEventsBackend when building the runtime.

Examples

Facade wiring through Spectra::builder() (requires the spectra crate with the tensorbase feature):

use std::sync::Arc;
use spectra::{Spectra, TensorBaseEventsBackend, TensorBaseMetricsBackend};

let url = "tcp://127.0.0.1:9528";
let spectra = Spectra::builder()
    .metrics_backend(Arc::new(TensorBaseMetricsBackend::connect(url).await?))
    .events_backend(Arc::new(TensorBaseEventsBackend::connect(url).await?))
    .build()?;

Implementations

impl TensorBaseMetricsBackend

pub async fn connect(url: &str) -> Result<TensorBaseMetricsBackend, Error>

Connect with a full TensorBase protocol URL and ensure metrics tables exist.

Accepts tcp://host:9528 (native) or an HTTP ClickHouse-compatible endpoint. The call is async and executes DDL.

Examples

use spectra_backend_tensorbase::TensorBaseMetricsBackend;

let backend = TensorBaseMetricsBackend::connect("tcp://127.0.0.1:9528").await?;

pub async fn connect_host(host: &str) -> Result<TensorBaseMetricsBackend, Error>

Connect using a host name and the default native port (9528).

Equivalent to connect with tcp://{host}:9528.

Examples

use spectra_backend_tensorbase::TensorBaseMetricsBackend;

let backend = TensorBaseMetricsBackend::connect_host("127.0.0.1").await?;

pub fn in_memory_stub() -> TensorBaseMetricsBackend

In-memory stub that preserves the TensorBase engine type for storage-contract tests.

Examples

use spectra_backend_tensorbase::TensorBaseMetricsBackend;
use spectra_core::{MetricsStorageBackend, StorageEngineType};

let backend = TensorBaseMetricsBackend::in_memory_stub();
assert_eq!(backend.engine_type(), StorageEngineType::TensorBase);

Trait Implementations

impl MetricsStorageBackend for TensorBaseMetricsBackend

fn engine_type(&self) -> StorageEngineType

Returns the engine type for this backend.

fn record_counter<'life0, 'life1, 'life2, 'async_trait>( &'life0 self, name: &'life1 str, labels: &'life2 Value, delta: i64, ts: DateTime<Utc>, ) -> Pin<Box<dyn Future<Output = Result<(), Error>> + Send + 'async_trait>>

where 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait, TensorBaseMetricsBackend: 'async_trait,

Records a counter increment.

fn record_gauge<'life0, 'life1, 'life2, 'async_trait>( &'life0 self, name: &'life1 str, labels: &'life2 Value, value: f64, ts: DateTime<Utc>, ) -> Pin<Box<dyn Future<Output = Result<(), Error>> + Send + 'async_trait>>

where 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait, TensorBaseMetricsBackend: 'async_trait,

Records a gauge sample.

fn record_metrics_batch<'life0, 'life1, 'async_trait>( &'life0 self, rows: &'life1 [MetricWriteRow], ) -> Pin<Box<dyn Future<Output = Result<(), Error>> + Send + 'async_trait>>

where 'life0: 'async_trait, 'life1: 'async_trait, TensorBaseMetricsBackend: 'async_trait,

Batch write metrics (default: per-row dispatch).

fn query_range<'life0, 'async_trait>( &'life0 self, query: MetricsQueryRange, ) -> Pin<Box<dyn Future<Output = Result<Vec<MetricPoint>, Error>> + Send + 'async_trait>>

where 'life0: 'async_trait, TensorBaseMetricsBackend: 'async_trait,

Queries a time range of metric points.