Object

ai.chronon.online

LRUCache

Related Doc: package online

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object LRUCache

Utility to create a cache with LRU semantics.

The original purpose of having an LRU cache in Chronon is to cache KVStore calls and decoded IRs in the Fetcher. This helps decrease to feature serving latency.

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def apply[KEY <: AnyRef, VALUE <: AnyRef](cacheName: String, maximumSize: Int = 10000): Cache[KEY, VALUE]

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    Build a bounded, thread-safe Caffeine cache that stores KEY-VALUE pairs.

    Build a bounded, thread-safe Caffeine cache that stores KEY-VALUE pairs.

    KEY

    The type of the key used to access the cache

    VALUE

    The type of the value stored in the cache

    cacheName

    Name of the cache

    maximumSize

    Maximum number of entries in the cache

    returns

    Caffeine cache

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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  7. def collectCaffeineCacheMetrics(metricsContext: Context, cache: Cache[_, _], cacheName: String): Unit

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    Report metrics for a Caffeine cache.

    Report metrics for a Caffeine cache. The "cache" tag is added to all metrics.

    metricsContext

    Metrics.Context for recording metrics

    cache

    Caffeine cache to get metrics from

    cacheName

    Cache name for tagging

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  18. def toString(): String

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  19. final def wait(): Unit

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  20. final def wait(arg0: Long, arg1: Int): Unit

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  21. final def wait(arg0: Long): Unit

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