trait TsdbBase extends StrictLogging
Core of time series database processing pipeline.
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- abstract type Collection[_]
Type of collection used in this TSDB instance and the DAO.
Type of collection used in this TSDB instance and the DAO. The default implementation uses Iterator as a collection type. Spark based implementation uses RDD.
- abstract type Result <: TsdbResultBase[Collection]
Abstract Value Members
- abstract def applyWindowFunctions(queryContext: QueryContext, keysAndValues: Collection[BatchDataset]): Collection[BatchDataset]
- abstract def calculatorFactory: ExpressionCalculatorFactory
- abstract def changelogDao: ChangelogDao
- abstract def createMetricCollector(query: Query, user: YupanaUser): MetricQueryCollector
- abstract def dao: TSDao[Collection, Long]
- abstract def externalLinkServices: Iterable[ExternalLinkService[_]]
- abstract val extractBatchSize: Int
Batch size for reading values from external links
- abstract def finalizeQuery(queryContext: QueryContext, rows: Collection[BatchDataset], metricCollector: MetricQueryCollector): Result
- abstract def linkService(catalog: ExternalLink): ExternalLinkService[_ <: ExternalLink]
- abstract def permissionService: PermissionService
- abstract def prepareQuery: (Query) => Query
- abstract val putBatchSize: Int
Batch size for writing values to external links
- abstract def registerExternalLink(catalog: ExternalLink, catalogService: ExternalLinkService[_ <: ExternalLink]): Unit
- abstract def schema: Schema
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- def fillPlaceholders(c: Condition, startTime: Time, params: IndexedSeq[Any]): Condition
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- val logger: Logger
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- def mapReduceEngine(metricCollector: MetricQueryCollector): MapReducible[Collection]
- def mergeCondition(facs: Seq[FlatAndCondition]): Option[Condition]
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- def processRows(queryContext: QueryContext, metricCollector: MetricQueryCollector, mr: MapReducible[Collection], rows: Collection[BatchDataset], startTime: Time, params: IndexedSeq[Any]): Result
- def put(dataPoints: Collection[DataPoint], user: YupanaUser = YupanaUser.ANONYMOUS): Unit
- def putBatch(table: Table, batch: BatchDataset, user: YupanaUser): Unit
- def putDataset(table: Table, dataset: Collection[BatchDataset], user: YupanaUser): Unit
- def putDataset(tables: Seq[Table], dataset: Collection[BatchDataset], user: YupanaUser): Unit
- def query(query: Query, startTime: Time = Time(System.currentTimeMillis()), params: IndexedSeq[Any] = IndexedSeq.empty, user: YupanaUser = YupanaUser.ANONYMOUS): Result
Query pipeline.
Query pipeline. Perform following stages:
- creates queries for DAO - call DAO query to get Collection of rows - fills the rows with external links values - extract KeyData and ValueData - apply value filters - window function application - apply aggregation: map, reduce, post-map - post reduce arithmetics - extract field values
The pipeline is not responsible for limiting. This means that collection have to be lazy, to avoid extra calculations if limit is defined.
- def readExternalLinks(queryContext: QueryContext, ds: BatchDataset): Unit
- def substituteLinks(flatAndConditions: Seq[FlatAndCondition], startTime: Time, user: YupanaUser, metricCollector: MetricQueryCollector): Seq[FlatAndCondition]
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- Deprecated
(Since version 9)