samueleresca

from quoracle import QuorumSystem, Node

if __name__ == '__main__':
    a, b, c = Node('a'), Node('b'), Node('c')
    majority = QuorumSystem(reads=a*b + b*c + a*c)
    print(majority.load(read_fraction=1))     # 2/3
    print(majority.capacity(read_fraction=1)) # 3/2

samueleresca

import time
from typing import Iterable

import pytest

from phi_accrual_failure_detector import PhiAccrualFailureDetector


class TestPhiAccrualFailureDetector:

samueleresca

import math
import time

from atomos.atomic import AtomicReference

from src._heartbeat_history import _HeartbeatHistory
from src._state import _State


class PhiAccrualFailureDetector:

samueleresca

import math
import time

from atomos.atomic import AtomicReference

from src._heartbeat_history import _HeartbeatHistory
from src._state import _State


class PhiAccrualFailureDetector:

samueleresca

from src._heartbeat_history import _HeartbeatHistory
from typing import Optional

class _State:
    """
    Represents the accrual failure detector's state. It is wraps the _HeartbeatHistory and the latest (most recent) timestamp.

    See: https://github.com/akka/akka/blob/0326e113879f08f39ca80667512cc960f267c81b/akka-remote/src/main/scala/akka/remote/PhiAccrualFailureDetector.scala#L120

    Attributes:

samueleresca

from __future__ import annotations
import math


class _HeartbeatHistory:
    """
    Represent the sample window defined in the φ Accrual failure detector paper.
    It stores a limited list of heartbeats in a list of length max_sample_size.

    See:

samueleresca

// Lets now assume that a single partition changes. We only need to recompute the state of this
// partition in order to update the metrics for the whole table.
DataFrame updatedUsManufacturers = LoadData(new[]
{
    new object[] {3, "ManufacturerDNew", "US"}, new object[] {4, null, "US"},
    new object[] {5, "ManufacturerFNew http://clickme.com", "US"},
});

// Recompute state of partition
InMemoryStateProvider updatedUsStates = new InMemoryStateProvider();

samueleresca

DataFrame dataSetDE = LoadDataSetDE();
DataFrame dataSetUS = LoadDataSetUS();
DataFrame dataSetCN = LoadDataSetCN();

// We initialize a new check for the following data fields
var check = new Check(CheckLevel.Warning, "generic check")
    .IsComplete("manufacturerName")
    .ContainsURL("manufacturerName", val => val == 0.0)
    .IsContainedIn("countryCode", new[] { "DE", "US", "CN" });

samueleresca

internal abstract class ScanShareableAnalyzer<S, M> : Analyzer<S, M>, ...
{
    ...

    public override Option<S> ComputeStateFrom(DataFrame dataFrame)
    {
        IEnumerable<Column> aggregations = AggregationFunctions();
        Row result = dataFrame
            .Agg(aggregations.First(), aggregations.Skip(1).ToArray())
            .Collect()

samueleresca

using Microsoft.Spark.Sql;
using Microsoft.Spark.Sql.Types;
using static Microsoft.Spark.Sql.Functions;

namespace deequ.Analyzers
{
    internal sealed class MaxLength : StandardScanShareableAnalyzer<MaxState>, IFilterableAnalyzer
    {
        public readonly string Column;
        public readonly Option<string> Where;