Expressiveness Benchmark

def rolling_average(data):
  data.sort(key=lambda v: v["time"])
  result = []
  for i, value in enumerate(data):
    end = value["time"]
    total, count = 0.0, 0
    for j in range(i, -1, -1):
      if data[j]["time"] <= end - 7:
        break
      total += data[j]["x"]; count += 1
    result.append(
      {"end_time": end,
       "average": total / count}
    )
  return result

def rolling_average(data):
  return [
    {"end_time": x["time"],
     "average": sum(vs) / len(vs)}
    for x in data
    for vs in [
      [y["x"] for y in data
              if y["time"] <= x["time"] and
                 y["time"] > x["time"] - 7]
    ]
  ]

def rolling_average(data):
  d = data.copy()

  data.time = pd.to_datetime(data.time * 10**9)
  data = (data.sort_values('time').set_index('time')
              .rolling(window='7s').mean())

  return pd.DataFrame.from_dict(
    {'end_time': d.sort_values('time').reset_index().time,
     'average': data.reset_index().x}
  )

library(slider)
rolling_average <- function(data) {
  data <- arrange(data, time)
  avgs <- unlist(slide_index(data$x, data$time, ~ mean(.x), .before = 6))
  data %>% mutate(end_time = time, average = avgs) %>% select(end_time, average)
}

SELECT
end.time as end_time,  AVG(other.x) as average
FROM data as end
JOIN data as other
ON other.time <= end.time and
   other.time > end.time - 7
GROUP BY end.time

.decl window(end_time: number, time: number)
window(end_time, t) :-
  data(end_time, _), data(t, _),
  t <= end_time, t > end_time - 7.
.decl bucket(end_time: number, total: float, n: float)
bucket(end_time, total, n) :- data(end_time, _),
  total = sum v : {data(t, v), window(end_time, t)},
      n = sum z : {data(t, _), window(end_time, t), z = 1.0}.
rolling_average(end_time, v) :-
  bucket(end_time, total, n), v = total / n.

get_avg: 
  {[t] (select avg(x) from data where time within (t - 6; t)) `x};
rolling_average: 
  select end_time: time, average: get_avg'[time] from data