misc(query): feature flag to exclude start time for range functions

core/src/main/resources/filodb-defaults.conf

@@ -318,6 +318,9 @@ filodb {
 
   query {
 
+    # feature flag to enable start time inclusive in window lookback
+    inclusive-range = true
+
     # feature flag for query result streaming
     streaming-query-results-enabled = false
 

core/src/main/scala/filodb.core/store/ChunkSetInfo.scala

@@ -435,7 +435,8 @@ class WindowedChunkIterator(rv: RawDataRangeVector, start: Long, step: Long, end
                             var curWindowEnd: Long = -1L,
                             var curWindowStart: Long = -1L,
                             private var readIndex: Int = 0,
-                            windowInfos: Buffer[ChunkSetInfoReader] = Buffer.empty[ChunkSetInfoReader])
+                            windowInfos: Buffer[ChunkSetInfoReader] = Buffer.empty[ChunkSetInfoReader],
+                            isInclusiveRange: Boolean = true)
 extends Iterator[ChunkSetInfoReader] {
   require(step > 0, s"Adjusted step $step not > 0")
   private val infos = rv.chunkInfos(start - window, end)
@@ -455,7 +456,8 @@ extends Iterator[ChunkSetInfoReader] {
     // advance window pointers and reset read index
     if (curWindowEnd == -1L) {
       curWindowEnd = start
-      curWindowStart = start - Math.max(window, 0)  // window cannot be below 0, ie start should never be > end
+      val windowDuration = if (isInclusiveRange) window else window - 1
+      curWindowStart = start - Math.max(windowDuration, 0)  // window cannot be below 0, ie start should never be > end
     } else {
       curWindowEnd += step
       curWindowStart += step

query/src/main/scala/filodb/query/exec/PeriodicSamplesMapper.scala

@@ -4,6 +4,7 @@ import com.typesafe.scalalogging.StrictLogging
 import monix.reactive.Observable
 import org.jctools.queues.SpscUnboundedArrayQueue
 
+import filodb.core.GlobalConfig.systemConfig
 import filodb.core.metadata.Column.ColumnType
 import filodb.core.query._
 import filodb.core.store.WindowedChunkIterator
@@ -196,6 +197,10 @@ final case class PeriodicSamplesMapper(startMs: Long,
   }
 }
 
+object FiloQueryConfig {
+  val isInclusiveRange = systemConfig.getBoolean("filodb.query.inclusive-range")
+}
+
 /**
  * A low-overhead iterator which works on one window at a time, optimally applying columnar techniques
  * to compute each window as fast as possible on multiple rows at a time.
@@ -213,7 +218,8 @@ extends WrappedCursor(rv.rows()) with StrictLogging {
   // Lazily open the iterator and obtain the lock. This allows one thread to create the
   // iterator, but the lock is owned by the thread actually performing the iteration.
   private lazy val windowIt = {
-    val it = new WindowedChunkIterator(rv, start, step, end, window, querySession.qContext)
+    val it = new WindowedChunkIterator(rv, start, step, end, window, querySession.qContext,
+      isInclusiveRange = FiloQueryConfig.isInclusiveRange)
     // Need to hold the shared lock explicitly, because the window iterator needs to
     // pre-fetch chunks to determine the window. This pre-fetching can force the internal
     // iterator to close, which would release the lock too soon.
@@ -378,7 +384,8 @@ class SlidingWindowIterator(raw: RangeVectorCursor,
     */
   private def shouldAddCurToWindow(curWindowStart: Long, cur: TransientRow): Boolean = {
     // cur is inside current window
-    cur.timestamp >= curWindowStart
+    val windowStart = if (FiloQueryConfig.isInclusiveRange) curWindowStart else curWindowStart + 1
+    cur.timestamp >= windowStart
   }
 
   /**
@@ -390,7 +397,8 @@ class SlidingWindowIterator(raw: RangeVectorCursor,
     * @param curWindowStart start time of the current window
     */
   private def shouldRemoveWindowHead(curWindowStart: Long): Boolean = {
-    (!windowQueue.isEmpty) && windowQueue.head.timestamp < curWindowStart
+    val windowStart = if (FiloQueryConfig.isInclusiveRange) curWindowStart else curWindowStart - 1
+    (!windowQueue.isEmpty) && windowQueue.head.timestamp < windowStart
   }
 }
 

query/src/main/scala/filodb/query/exec/rangefn/RateFunctions.scala

@@ -5,6 +5,7 @@ import spire.syntax.cfor._
 import filodb.core.query.{QueryConfig, TransientHistRow, TransientRow}
 import filodb.memory.format.{vectors => bv, BinaryVector, CounterVectorReader, MemoryReader, VectorDataReader}
 import filodb.memory.format.BinaryVector.BinaryVectorPtr
+import filodb.query.exec.FiloQueryConfig
 
 object RateFunctions {
 
@@ -188,8 +189,12 @@ abstract class ChunkedRateFunctionBase extends CounterChunkedRangeFunction[Trans
   override def apply(windowStart: Long, windowEnd: Long, sampleToEmit: TransientRow): Unit = {
     if (highestTime > lowestTime) {
       // NOTE: It seems in order to match previous code, we have to adjust the windowStart by -1 so it's "inclusive"
+      val curWindowStart = if (FiloQueryConfig.isInclusiveRange)
+                             windowStart
+                           else
+                             windowStart - 1
       val result = RateFunctions.extrapolatedRate(
-                     windowStart, windowEnd, numSamples,
+                    curWindowStart, windowEnd, numSamples,
                      lowestTime, lowestValue,
                      highestTime, highestValue,
                      isCounter, isRate)
@@ -284,9 +289,15 @@ abstract class HistogramRateFunctionBase extends CounterChunkedRangeFunction[Tra
       // TODO: handle case where schemas are different and we need to interpolate schemas
       if (highestValue.buckets == lowestValue.buckets) {
         val rateArray = new Array[Double](lowestValue.numBuckets)
+
+        val curWindowStart = if (FiloQueryConfig.isInclusiveRange)
+                                windowStart
+                             else
+                                windowStart - 1
+
         cforRange { 0 until rateArray.size } { b =>
           rateArray(b) = RateFunctions.extrapolatedRate(
-                           windowStart, windowEnd, numSamples,
+                          curWindowStart, windowEnd, numSamples,
                            lowestTime, lowestValue.bucketValue(b),
                            highestTime, highestValue.bucketValue(b),
                            isCounter, isRate)
@@ -329,8 +340,14 @@ class RateOverDeltaChunkedFunctionD extends ChunkedDoubleRangeFunction {
                                 endRowNum: Int): Unit =
     sumFunc.addTimeDoubleChunks(doubleVectAcc, doubleVect, doubleReader, startRowNum, endRowNum)
 
-  override def apply(windowStart: Long, windowEnd: Long, sampleToEmit: TransientRow): Unit =
-    sampleToEmit.setValues(windowEnd, sumFunc.sum / (windowEnd - (windowStart)) * 1000)
+  override def apply(windowStart: Long, windowEnd: Long, sampleToEmit: TransientRow): Unit = {
+    val curWindowStart = if (FiloQueryConfig.isInclusiveRange)
+                            windowStart
+                         else
+                            windowStart - 1
+    sampleToEmit.setValues(windowEnd, sumFunc.sum / (windowEnd - (curWindowStart)) * 1000)
+  }
+
   override def apply(endTimestamp: Long, sampleToEmit: TransientRow): Unit = ???
 }
 
@@ -346,8 +363,13 @@ class RateOverDeltaChunkedFunctionL extends ChunkedLongRangeFunction {
                               endRowNum: Int): Unit =
     sumFunc.addTimeChunks(longVectAcc, longVect, longReader, startRowNum, endRowNum)
 
-  override def apply(windowStart: Long, windowEnd: Long, sampleToEmit: TransientRow): Unit =
-    sampleToEmit.setValues(windowEnd, sumFunc.sum / (windowEnd - (windowStart)) * 1000)
+  override def apply(windowStart: Long, windowEnd: Long, sampleToEmit: TransientRow): Unit = {
+    val curWindowStart = if (FiloQueryConfig.isInclusiveRange)
+                            windowStart
+                         else
+                            windowStart - 1
+    sampleToEmit.setValues(windowEnd, sumFunc.sum / (windowEnd - (curWindowStart)) * 1000)
+  }
 
   override def apply(endTimestamp: Long, sampleToEmit: TransientRow): Unit = ???
 }
@@ -376,5 +398,4 @@ class RateOverDeltaChunkedFunctionH(var h: bv.MutableHistogram = bv.Histogram.em
     hFunc.addTimeChunks(vectAcc, vectPtr, reader, startRowNum, endRowNum)
 
   def apply(endTimestamp: Long, sampleToEmit: TransientHistRow): Unit = ???
-}
-
+}