YQ Connector: support managed ClickHouse

Со стороны dqrun можно обратиться к инстансу коннектора, который работает на streaming стенде, и извлечь данные из облачного CH.

1 files changed, 171 insertions, 0 deletions

diff --git a/contrib/libs/llvm14/tools/llvm-exegesis/lib/Clustering.h b/contrib/libs/llvm14/tools/llvm-exegesis/lib/Clustering.h
new file mode 100644
index 0000000000..a4da3af774
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+++ b/contrib/libs/llvm14/tools/llvm-exegesis/lib/Clustering.h
@@ -0,0 +1,171 @@
+//===-- Clustering.h --------------------------------------------*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+///
+/// \file
+/// Utilities to compute benchmark result clusters.
+///
+//===----------------------------------------------------------------------===//
+
+#ifndef LLVM_TOOLS_LLVM_EXEGESIS_CLUSTERING_H
+#define LLVM_TOOLS_LLVM_EXEGESIS_CLUSTERING_H
+
+#include "BenchmarkResult.h"
+#include "llvm/ADT/Optional.h"
+#include "llvm/Support/Error.h"
+#include <limits>
+#include <vector>
+
+namespace llvm {
+namespace exegesis {
+
+class InstructionBenchmarkClustering {
+public:
+  enum ModeE { Dbscan, Naive };
+
+  // Clusters `Points` using DBSCAN with the given parameters. See the cc file
+  // for more explanations on the algorithm.
+  static Expected<InstructionBenchmarkClustering>
+  create(const std::vector<InstructionBenchmark> &Points, ModeE Mode,
+         size_t DbscanMinPts, double AnalysisClusteringEpsilon,
+         const MCSubtargetInfo *SubtargetInfo = nullptr,
+         const MCInstrInfo *InstrInfo = nullptr);
+
+  class ClusterId {
+  public:
+    static ClusterId noise() { return ClusterId(kNoise); }
+    static ClusterId error() { return ClusterId(kError); }
+    static ClusterId makeValid(size_t Id, bool IsUnstable = false) {
+      return ClusterId(Id, IsUnstable);
+    }
+    static ClusterId makeValidUnstable(size_t Id) {
+      return makeValid(Id, /*IsUnstable=*/true);
+    }
+
+    ClusterId() : Id_(kUndef), IsUnstable_(false) {}
+
+    // Compare id's, ignoring the 'unstability' bit.
+    bool operator==(const ClusterId &O) const { return Id_ == O.Id_; }
+    bool operator<(const ClusterId &O) const { return Id_ < O.Id_; }
+
+    bool isValid() const { return Id_ <= kMaxValid; }
+    bool isUnstable() const { return IsUnstable_; }
+    bool isNoise() const { return Id_ == kNoise; }
+    bool isError() const { return Id_ == kError; }
+    bool isUndef() const { return Id_ == kUndef; }
+
+    // Precondition: isValid().
+    size_t getId() const {
+      assert(isValid());
+      return Id_;
+    }
+
+  private:
+    ClusterId(size_t Id, bool IsUnstable = false)
+        : Id_(Id), IsUnstable_(IsUnstable) {}
+
+    static constexpr const size_t kMaxValid =
+        (std::numeric_limits<size_t>::max() >> 1) - 4;
+    static constexpr const size_t kNoise = kMaxValid + 1;
+    static constexpr const size_t kError = kMaxValid + 2;
+    static constexpr const size_t kUndef = kMaxValid + 3;
+
+    size_t Id_ : (std::numeric_limits<size_t>::digits - 1);
+    size_t IsUnstable_ : 1;
+  };
+  static_assert(sizeof(ClusterId) == sizeof(size_t), "should be a bit field.");
+
+  struct Cluster {
+    Cluster() = delete;
+    explicit Cluster(const ClusterId &Id) : Id(Id) {}
+
+    const ClusterId Id;
+    // Indices of benchmarks within the cluster.
+    std::vector<int> PointIndices;
+  };
+
+  ClusterId getClusterIdForPoint(size_t P) const {
+    return ClusterIdForPoint_[P];
+  }
+
+  const std::vector<InstructionBenchmark> &getPoints() const { return Points_; }
+
+  const Cluster &getCluster(ClusterId Id) const {
+    assert(!Id.isUndef() && "unlabeled cluster");
+    if (Id.isNoise()) {
+      return NoiseCluster_;
+    }
+    if (Id.isError()) {
+      return ErrorCluster_;
+    }
+    return Clusters_[Id.getId()];
+  }
+
+  const std::vector<Cluster> &getValidClusters() const { return Clusters_; }
+
+  // Returns true if the given point is within a distance Epsilon of each other.
+  bool isNeighbour(const std::vector<BenchmarkMeasure> &P,
+                   const std::vector<BenchmarkMeasure> &Q,
+                   const double EpsilonSquared_) const {
+    double DistanceSquared = 0.0;
+    for (size_t I = 0, E = P.size(); I < E; ++I) {
+      const auto Diff = P[I].PerInstructionValue - Q[I].PerInstructionValue;
+      DistanceSquared += Diff * Diff;
+    }
+    return DistanceSquared <= EpsilonSquared_;
+  }
+
+private:
+  InstructionBenchmarkClustering(
+      const std::vector<InstructionBenchmark> &Points,
+      double AnalysisClusteringEpsilonSquared);
+
+  Error validateAndSetup();
+
+  void clusterizeDbScan(size_t MinPts);
+  void clusterizeNaive(const MCSubtargetInfo &SubtargetInfo,
+                       const MCInstrInfo &InstrInfo);
+
+  // Stabilization is only needed if dbscan was used to clusterize.
+  void stabilize(unsigned NumOpcodes);
+
+  void rangeQuery(size_t Q, std::vector<size_t> &Scratchpad) const;
+
+  bool areAllNeighbours(ArrayRef<size_t> Pts) const;
+
+  const std::vector<InstructionBenchmark> &Points_;
+  const double AnalysisClusteringEpsilonSquared_;
+
+  int NumDimensions_ = 0;
+  // ClusterForPoint_[P] is the cluster id for Points[P].
+  std::vector<ClusterId> ClusterIdForPoint_;
+  std::vector<Cluster> Clusters_;
+  Cluster NoiseCluster_;
+  Cluster ErrorCluster_;
+};
+
+class SchedClassClusterCentroid {
+public:
+  const std::vector<PerInstructionStats> &getStats() const {
+    return Representative;
+  }
+
+  std::vector<BenchmarkMeasure> getAsPoint() const;
+
+  void addPoint(ArrayRef<BenchmarkMeasure> Point);
+
+  bool validate(InstructionBenchmark::ModeE Mode) const;
+
+private:
+  // Measurement stats for the points in the SchedClassCluster.
+  std::vector<PerInstructionStats> Representative;
+};
+
+} // namespace exegesis
+} // namespace llvm
+
+#endif // LLVM_TOOLS_LLVM_EXEGESIS_CLUSTERING_H