Django database planning - time series data

Django database planning - time series data

By : user2952906
Date : November 19 2020, 03:59 PM
it fixes the issue One thing that may help you with HUGE tables is partitioning. Judging by the postgresql tag that you set for your question, take a look here: http://www.postgresql.org/docs/9.1/static/ddl-partitioning.html
But for a start I would go with a one, simple table, supported by a reasonable set of indexes. From what I understand, each data entry in the table will be identified by race session id, player id and time indicator. Those columns should be covered with indexes according to your querying requirements.
code :
class RaceTelemetryData(models.Model):
  user = models.ForeignKey(..., index_db=True)
  race = models.ForeignKey(YourRaceModel, index_db=True)
  time = models.IntegerField()
  gas = models.IntegerField()
  speed = models.SmallIntegerField()
  # and so on...

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Optaplanner : Planning entity and Planning variable at the same time?

Optaplanner : Planning entity and Planning variable at the same time?

By : Алексей Юманов
Date : March 29 2020, 07:55 AM
may help you . The TSP and Vehicle Routing example already have planning values that are planning entities too, although they both use a chained=true variable. Because chained=true, it implies that no 2 planning entities have the same planning variable (and all are directly or indirectly connected to an anchor). See docs section "chained planning variables".
I suspect in your case, that you don't want chains, but instead a tree, a directed graph or an undirected graph. An undirected graph (= a graph which allows cycles) is problematic for Construction Heuristics, because out-of-the-box they can't construct a graph with a cycle. As for a tree and a directed graph: it should work in theory, but you might need custom moves (see docs) to have the moves be efficient. In the future, we'd like to support a tree structure similarly to how we support chains (for job shop scheduling).
Time series database with time-weighted-average aggregation function for irregular time series?

Time series database with time-weighted-average aggregation function for irregular time series?

By : Yash Verma
Date : March 29 2020, 07:55 AM
it should still fix some issue OpenTSDB performs aggregation across all series in a query at the time(s) implied by the query. For any series not having a data value at a timestamp, it linearly interpolates a value from the values before and after. It does this "upsampling" at query time -- the original data is always stored as it was when it arrived. You can perform a trailing windowed time average, but not an exponentially weighted moving average (I believe that is what you meant by time-weighted?)
How to add visualisation for time planning (What Django package to reuse)?

How to add visualisation for time planning (What Django package to reuse)?

By : Kaylee
Date : March 29 2020, 07:55 AM
will help you Since I got no answers I will answer my own question . The decision was to try to integrate with some of the existing tools without directly embedding then into our project. First, one we are looking at Trello/taiga-back
Is exist this type of database? (like time-series db, can get specific time point data...)

Is exist this type of database? (like time-series db, can get specific time point data...)

By : Omar Fernando Gonzal
Date : March 29 2020, 07:55 AM
will be helpful for those in need You are looking for the last status for each user before a given point in time. In SQL Server, you can use window functions:
code :
select count(*)
from (select t.*,
             row_number() over (partition by user_key order by updated_date desc) as seqnum
      from t
      where updated_date < @datetime
     ) t
where seqnum = 1 and status = 1;
Django Database Structure for Time Series Data?

Django Database Structure for Time Series Data?

By : user3414639
Date : March 29 2020, 07:55 AM
With these it helps The standard approach here would be to use Django models (translated to tables in the db):
Buoy BuoyData
code :
from django.contrib.gis.db import models

class Buoy(models.Model):
    Represents a Buoy
    location = models.PointField()
    name = models.CharField(max_length=200, null=True, blank=True)

class BuoyData(models.Model):
    Represents a single Buoy datapoint
    buoy = models.ForeignKey(Buoy, related_name='data')
    date = models.DateTimeField()
    # swell size in feet
    swell_size_ft = models.DecimalField(decimal_places=2, max_digits=5)
from django_pandas.io import read_frame

buoy = Buoy.objects.get(id=10)
data_queryset = buoy.data.all()
df = read_frame(data_queryset)

# or another example
import datetime
from django.utils import timezone
from django.contrib.gis.geos import Point

lat = 36.123
lng = -121.123
# notice how lng is x, lat is y
location = Point(lng, lat)

time_four_days_ago = timezone.now() - datetime.timedelta(days=4)

closest_buoy = Buoy.objects.distance(location).order_by('distance')[0]
data_queryset = buoy.data.filter(date__gte=time_four_days_ago)

# if there are a lot of fields on your BuoyData model,
# and you only need a few, .values will be more performant
data_values = data_queryset.values('date', 'swell_size_ft')
df = read_frame(data_values)
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