PGConf India 2023

SELECT
  'PGConf' as conference_name,
  2023 as edition,
  'Introduction to Data Science' as workshop_title,
  'Jônatas Davi Paganini' as author,
  'jonatas@timescale.com' as author_mail

Welcome

Timescaledb

&

Toolkit

Jônatas Davi Paganini

@jonatasdp

* Postgresql since 2004.

Backend developer
Ruby/Shell/Postgresql/Vim
- Developer Advocate at Timescale

twitter: @jonatasdp

github: @jonatas

Agenda

Introduction to Data Science - 1h
Exploring the Time Series dataset - 1h
Practical exercises with the dataset - 1h
Knowledge sharing - 0.5h

we can have short breaks each hour.

Introduction

Data Science and its Applications in various domains
- Focus of Today will be a weather analysis.
Introduction to SQL and Postgresql
- Overview of SQL, its syntax and use cases.
- Introduction to Postgresql database and its features.
Overview of Timescaledb and its Features
- Explanation of what Timescaledb is and how it extends Postgresql for time-series data.
- Overview of the features and benefits of using Timescaledb for time-series data analysis.

Data Science

Data science is an interdisciplinary field that involves using statistical and computational methods to extract insights and knowledge from data.

Applications

In the context of weather analysis, data science can be used to

analyze weather metrics
predict future weather patterns

We're not going to focus on prediction Today.

Data

Source is from open weather:

https://openweathermap.org

Free data from entire world.
Free API.
Statistics from anywhere.
Time-series data.

The focus will be weather metrics.

Interface

Interact with open weather dataset via psql:

psql open_weather

you can use your favorite tool if you want ;)

Create

Use createdb open_weather in case you don't have it yet.

you can use your favorite tool if you want ;)

Extension

If you don't have timescaledb installed on your database, enable the extension:

CREATE EXTENSION timescaledb;

You can skip this step if you're using Timescale Cloud or Timescale docker images.

Download

Download the repository:

https://github.com/jonatas/sql-data-science-training

Use the schema.sql file for the next few steps if you want. Our steps will be:

Create table
Create indices
Transform the table into hypertable

Table

CREATE TABLE public.weather_metrics (
    "time" timestamp without time zone NOT NULL,
    timezone_shift integer,
    city_name text,
    temp_c double precision,
    feels_like_c double precision,
    temp_min_c double precision,
    temp_max_c double precision,
    pressure_hpa double precision,
    humidity_percent double precision,
    wind_speed_ms double precision,
    wind_deg integer,
    rain_1h_mm double precision,
    rain_3h_mm double precision,
    snow_1h_mm double precision,
    snow_3h_mm double precision,
    clouds_percent integer,
    weather_type_id integer
);

Hypertable

SELECT create_hypertable('weather_metrics', 'time',
  chunk_time_interval => INTERVAL '1 month');

Import

We'll use the CSVs in the data folder from

https://github.com/jonatas/sql-data-science-training.

\i schema.sql
\COPY weather_metrics FROM './data/nairobi.csv' DELIMITER ',' CSV HEADER;
\COPY weather_metrics FROM './data/new_york.csv' DELIMITER ',' CSV HEADER;
\COPY weather_metrics FROM './data/toronto.csv' DELIMITER ',' CSV HEADER;
\COPY weather_metrics FROM './data/stockholm.csv' DELIMITER ',' CSV HEADER;
\COPY weather_metrics FROM './data/princeton.csv' DELIMITER ',' CSV HEADER;
\COPY weather_metrics FROM './data/vienna.csv' DELIMITER ',' CSV HEADER;

copy commands should be executed line by line

Timing

On psql we can enable timing to check the performance of every command:

\timing

Counting

SELECT count(1) FROM weather_metrics;

Approx. row count

Timescaledb offers a different counting approach that is very approximate to real counter.

SELECT approximate_row_count('weather_metrics');

granularity

how many records are available per year?

select time_bucket('1 year', time) as x,
  count(*) as y,
  'bar' as type
  from weather_metrics
  group by 1,3
  order by 1

Check

Describe the weather_metrics table:

\d weather_metrics

5WH

The Hypertable 5WH!

Who: The timescaledb extension
What: Hypertable
When: you need to handle time-series data (insert, select, update, delete)
Where: In your PostgreSQL database
Why: to optimize time-series throughtput
How: using table partitions to compress, parallelize and manage smaller chunks of data.

\d+ weather_metrics

Explore

Let's start exploring the Time Series Data and answer a few questions.

Questions

Answer with sql

How to adapt the code to work the city time zone?
How many different cities are available?
When the data starts and when it ends?
How many records we have per city?
What was the average temperature of New York in January in the last 10 years.
What is the hottest and coldest city we're tracking?
What is the city that rains more?
Choose a city and investigate the season of the city?

Time zone

SELECT time +  timezone_shift::text::interval AS time,
  temp_c AS temperature,
  city_name AS city
FROM weather_metrics ORDER BY 1 DESC LIMIT 5;

Distinct

What are the name of the cities available?

SELECT DISTINCT city_name FROM weather_metrics;

Count

How many different cities are available?

SELECT COUNT(DISTINCT city_name) FROM weather_metrics;

Min / Max

When the data starts and when it ends?

SELECT MIN(time), MAX(time) FROM weather_metrics;

Group

How many records we have per city?

SELECT city_name, count(*) FROM weather_metrics GROUP BY 1;

Avg

What was the average temperature over all times?

select city_name, avg(temp_c) from weather_metrics  group by 1;

Time Series

Time Series Analysis in SQL and Timescaledb

What was the average temperature of NY in January of 2022?

select avg(temp_c) from weather_metrics
where city_name = 'New York'
and time between '2022-01-01' and '2022-01-31';

Year bucket

What was the average temperature of New York in January in the last 10 years.

Order by

What is the hottest and coldest city we're tracking?

Sum

What is the city that rains more?

Season

Choose a city and investigate the season of the city?

Views

Create views for:

ny view: fix the time zone shift
ny_winter: filter only winter days
ny_summer: filter only summer days

Use CTEs for minor queries that you're not going to explore further.

Partitions

Hypertable will partition date by time interval.

Let's run EXPLAIN ANALYZE in the previous query:

EXPLAIN ANALYZE
select avg(temp_c) from weather_metrics
where city_name = 'New York'
and time between '2022-01-01' and '2022-01-31';

Explain

Understanding a bit of the execution plan:

EXPLAIN SELECT count(1) FROM weather_metrics;

Partitioned tables can divide and conquer!

->  Parallel Append  (cost=0.29..56957.24 rows=1023114 width=0)                                                                                    │
│  ->  Parallel Index Only Scan using _hyper_1_...idx on _hyper_1_...

time bucket

Get average of temperature grouped by one hour.

SELECT time_bucket('1 hour', time) AS bucket,
  avg(temp_c)
FROM weather_metrics
 WHERE city_name = 'New York'
AND time BETWEEN '2022-06-01' AND '2022-06-02'
GROUP BY 1 ORDER BY 1;

The time_bucket also supports timestamps with time zones.

Min / Max

Now, let's get a bit more details adding the min AND max:

SELECT time_bucket('1 hour'::interval, time) AS bucket,
  avg(temp_c)::numeric(4,2),
  min(temp_c), max(temp_c)
FROM weather_metrics
WHERE city_name = 'New York'
  AND time BETWEEN '2022-06-01' AND '2022-06-02'
GROUP BY 1 ORDER BY 1;

OHLC

Candlestick pattern: capture values grouped by a timeframe.

OHLC = Open, High, Low, Close

SELECT time_bucket('1 hour'::interval, time) AS bucket,
  first(temp_c, time) as open,
  max(temp_c) as high,
  min(temp_c) as low,
  last(temp_c, time) as close
FROM weather_metrics
WHERE city_name = 'New York'
  AND time BETWEEN '2022-06-01' AND '2022-06-02'
GROUP BY 1 ORDER BY 1;

You can also use first and last to find the open/close inside a time_bucket.

Later we'll see the candlestick_agg from the toolkit extension.

Stddev

Now, we can also check the standard deviation:

SELECT time_bucket('1 hour'::interval, time) AS bucket,
  avg(temp_c)::numeric(4,2),
  min(temp_c), max(temp_c), stddev(temp_c)
FROM weather_metrics
WHERE city_name = 'New York'
  AND time BETWEEN '2022-06-01' AND '2022-06-02'
GROUP BY 1 ORDER BY 1;

array_agg

Now going deep into individual values inside this hour:

 SELECT time_bucket('1 hour'::interval, time) AS bucket,
array_agg( temp_c)
FROM weather_metrics
 WHERE city_name = 'New York'
AND time BETWEEN '2022-06-01 00:00:00' AND '2022-06-01 01:00:00'
GROUP BY 1 ORDER BY 1;

Toolkit

Enable the toolkit extension:

CREATE EXTENSION timescaledb_toolkit;

Statistic functions

percentile_agg

To get the percentile_agg function an overview:

SELECT time_bucket('1 hour'::interval, time) AS bucket,
   percentile_agg( temp_c)
FROM weather_metrics
 WHERE city_name = 'New York'
AND time BETWEEN '2022-06-01 00:00:00' AND '2022-07-01 01:00:00'
GROUP BY 1 ORDER BY 1;

The functions with _agg suffix' indicates that several statistical aggregates can be pre-computed and save computing later.

They also support the pipeline operator.

Median from percentile

Extracting the median from the percentile:

SELECT time_bucket('1 month'::interval, time) AS bucket,
    approx_percentile(0.5, percentile_agg( temp_c)) as median
  FROM weather_metrics
  WHERE city_name = 'New York'
    AND time BETWEEN '2021-06-01 00:00:00' AND '2022-07-01 01:00:00'
  GROUP BY 1 ORDER BY 1;

Pipeline

Allows functional programming in SQL with the pipeline operator ->.

SELECT time_bucket('1 month'::interval, time) AS bucket,
    percentile_agg( temp_c) -> approx_percentile(0.5) as median
  FROM weather_metrics
  WHERE city_name = 'New York'
    AND time BETWEEN '2021-06-01 00:00:00' AND '2022-07-01 01:00:00'
  GROUP BY 1 ORDER BY 1;

quartiles

Now, getting quartiles AND median from percentiles:

SELECT time_bucket('1 month'::interval, time) AS bucket,
    approx_percentile(0.25, percentile_agg( temp_c)) AS q_1,
    approx_percentile(0.5, percentile_agg( temp_c)) AS median,
    approx_percentile(0.75, percentile_agg( temp_c)) AS q3
FROM weather_metrics
 WHERE city_name = 'New York'
AND time BETWEEN '2021-06-01 00:00:00' AND '2022-06-01 01:00:00'
GROUP BY 1 ORDER BY 1;

CTE

Pre-compute aggregations with CTE can reuse the previous calculated percentile_agg:

WITH one_month AS (
  SELECT time_bucket('1 month'::interval, time) AS bucket,
    percentile_agg( temp_c)
  FROM weather_metrics
  WHERE city_name = 'New York'
    AND time BETWEEN '2021-06-01 00:00:00' AND '2022-07-01 01:00:00'
  GROUP BY 1 ORDER BY 1
)
SELECT bucket,
  approx_percentile(0.25, percentile_agg) AS q_1,
  approx_percentile(0.5, percentile_agg) AS median,
  approx_percentile(0.75, percentile_agg) AS q3
FROM one_month;

Stats aggs

Statistical aggregates in one or two dimensions to pre-compute statistics summary.

SELECT time_bucket('1 hour'::interval, time) AS bucket,
   stats_agg( temp_c) AS hourly_agg
FROM weather_metrics
 WHERE city_name = 'New York'
AND time BETWEEN '2022-06-01 00:00:00' AND '2022-07-01 01:00:00'
GROUP BY 1 ORDER BY 1

Average

Compute an average from stats aggs is very easy:

 SELECT time_bucket('1 hour'::interval, time) AS bucket,
   average(stats_agg( temp_c)) AS hourly_average
FROM weather_metrics
 WHERE city_name = 'New York'
AND time BETWEEN '2022-06-01 00:00:00' AND '2022-07-01 01:00:00'
GROUP BY 1 ORDER BY 1

Alias

Using CTE to reuse the stats aggs pre-computed data:

WITH agg AS (
  SELECT time_bucket('1 hour'::interval, time) AS bucket,
    stats_agg( temp_c)
  FROM weather_metrics
  WHERE city_name = 'New York'
  AND time BETWEEN '2022-06-01 00:00:00' AND '2022-07-01 01:00:00'
  GROUP BY 1
  ORDER BY 1
)
SELECT bucket, average(stats_agg) FROM agg;

Rollup

Rollup can combine stats aggs in different time frames:

WITH hourly AS (
  SELECT time_bucket('1 hour'::interval, time) AS hour_bucket,
    stats_agg( temp_c)
  FROM weather_metrics
  WHERE city_name = 'New York'
  AND time between '2022-06-01 00:00:00' AND '2022-07-01 01:00:00'
  GROUP BY 1 ORDER BY 1
)
SELECT time_bucket('1 day', hour_bucket),
  average(rollup(stats_agg))
FROM hourly GROUP BY 1;

cascade

Cascading rollups can reuse previous stats aggs:

WITH hourly AS ( SELECT time_bucket('1 hour'::interval, time) AS bucket,
    stats_agg( temp_c) AS hourly_agg
  FROM weather_metrics
  WHERE city_name = 'New York'
    AND time BETWEEN '2021-06-01 00:00:00' AND '2022-07-01 01:00:00'
  GROUP BY 1 ORDER BY 1
),
daily AS ( SELECT time_bucket('1 day', bucket) AS bucket,
    rollup(hourly_agg) AS daily_agg
  FROM hourly GROUP BY 1
),
monthly AS ( SELECT time_bucket('1 month', bucket) AS bucket,
   rollup(daily_agg) AS monthly_agg
 FROM daily GROUP BY 1
)
SELECT bucket, average(monthly_agg) from monthly;

Variance

Adding variance AND stddev without expensive computing process:

-- previous stats aggs example
SELECT bucket,
  average(monthly_agg),
  variance(monthly_agg),
  stddev(monthly_agg)
FROM monthly;

num vals

Querying number of values from pre-computed stats aggs:

WITH hourly AS (
  SELECT time_bucket('1 hour'::interval, time) AS bucket,
    stats_agg( temp_c) AS hourly_agg
  FROM weather_metrics
  WHERE city_name = 'New York'
  AND time BETWEEN '2023-02-01 00:00:00' AND '2023-02-01 12:00:00'
  GROUP BY 1 ORDER BY 1
)
SELECT bucket, average(hourly_agg), num_vals(hourly_agg) from hourly;

CAggs

AKA Continuous Aggregates ;)

Materialized views for hypertables.

CREATE MATERIALIZED VIEW ny_hourly_agg
WITH (timescaledb.continuous) AS
SELECT time_bucket('1 hour'::interval, time) AS bucket,
   stats_agg( temp_c) AS hourly_agg
FROM weather_metrics
 WHERE city_name = 'New York'
GROUP BY 1;

Materialized data can be combined with real time data from open timeframes.

caggs^2?

CREATE MATERIALIZED VIEW ny_daily_agg
WITH (timescaledb.continuous) AS
SELECT time_bucket('1 day',bucket),
rollup(hourly_agg) AS daily_agg
FROM ny_hourly_agg group by 1;

Candlesticks

CREATE MATERIALIZED VIEW weather_hourly
WITH (timescaledb.continuous) AS
    SELECT time_bucket('1 hour'::interval, time),
      symbol,
      toolkit_experimental.candlestick_agg(time, temp_c)
    FROM weather_metrics
    GROUP BY 1,2 WITH DATA;

View

SELECT symbol, time_bucket,
  toolkit_experimental.open(candlestick),
  toolkit_experimental.high(candlestick),
  toolkit_experimental.low(candlestick),
  toolkit_experimental.close(candlestick),
  toolkit_experimental.open_time(candlestick),
  toolkit_experimental.high_time(candlestick),
  toolkit_experimental.low_time(candlestick),
  toolkit_experimental.close_time(candlestick),
  toolkit_experimental.volume(candlestick),
  toolkit_experimental.vwap(candlestick)
FROM weather_hourly;

Correlation

WITH ny AS (
    SELECT
        time_bucket ('1 month', time),
        avg(temp_c)
    FROM
        weather_metrics
    WHERE
        city_name = 'New York'
        AND time BETWEEN '2010-01-01' AND '2021-01-01'
    GROUP BY 1
),
nai AS (
    SELECT
        time_bucket ('1 month', time),
        avg(temp_c)
    FROM
        weather_metrics
    WHERE
        city_name = 'Nairobi'
        AND time BETWEEN '2010-01-01' AND '2021-01-01'
    GROUP BY 1
)
SELECT
    time_bucket ('1 y', ny.time_bucket),
    covariance(stats_agg(ny.avg, nai.avg)),
    corr(stats_agg(ny.avg, nai.avg))
FROM ny
    JOIN nai ON ny.time_bucket = nai.time_bucket
GROUP BY 1;

Correlation matrix

Enable tablefunc to use crosstab:

CREATE EXTENSION tablefunc;

Combining pairs

WITH city_names AS (
  SELECT DISTINCT city_name as name
    FROM weather_metrics order by 1
)
SELECT a.name as first, b.name as second
  FROM city_names a
  JOIN city_names b ON true;

Crosstab

SELECT * FROM crosstab($$
        WITH city_names AS (
                SELECT DISTINCT city_name as name
                FROM weather_metrics order by 1
        ),
pairs as (
  SELECT a.name as first, b.name as second
  FROM city_names a
  JOIN city_names b ON true
),
summary AS (
    SELECT time_bucket('1 h', time), city_name,
        avg(temp_c)
    FROM weather_metrics
    WHERE time BETWEEN '2010-01-01' AND '2021-01-01'
    GROUP BY 1,2
ORDER BY 1,2)
SELECT
    a.city_name as first, b.city_name as second,
    corr(stats_agg(a.avg, b.avg))
FROM pairs
JOIN summary a ON (pairs.first = a .city_name)
JOIN summary b ON (pairs.second = b.city_name AND a.time_bucket = b.time_bucket)
--WHERE b.city_name = 'New York' and a.city_name = 'Nairobi'
GROUP BY 1,2
 order by 1, 2
$$::text,
'select distinct city_name from weather_metrics order by 1'::text
) as ct(city_name text,
  "Nairobi" double precision,
  "New York" double precision,
  "Princeton" double precision,
  "Stockholm" double precision,
  "Toronto" double precision,
  "Vienna" double precision);

Plotting

Time to chart!

ruby preview.rb training.md "postgres://jonatasdp@localhost:5432/pgconf-2023"

X & Y

SELECT
  x, random() as y
FROM
  generate_series(
  TIMESTAMP '2000-01-01 00:00:00',
  TIMESTAMP '2000-01-01 00:01:00',
INTERVAL '1 second') x

type

Type will refer to type chart.

SELECT
'bar' as type,
   array_agg(random() * 100) as y,
  array_agg(g) as x
FROM
  generate_series(
    now() - INTERVAL '1 hour',
    now(),
    INTERVAL '1 minute') g group by 1

title

Title will inject the title in the layout.

select
  'Total records per city' as title,
  'bar' as type,
  city_name as x,
  count(*) y from weather_metrics group by 1,2,3;

name

Name will make it the series name.

WITH resume as (
  select city_name as name,
  time_bucket('1 hour', time) as x,
  avg(temp_c) as y
  from weather_metrics
  where time between '2022-01-01' and '2023-01-02'
  group by 1,2
  order by 1,2
)
select name, array_agg(x) as x, array_agg(y) as y
from resume
group by 1

avg

select time_bucket('1 month', time) as x,
  avg(temp_c) as y
  from weather_metrics
  where time between '2022-01-01' and '2023-01-02'
  and city_name = 'New York'
  group by 1
  order by 1;

avg hour

select time_bucket('1 hour', time) as x,
  avg(temp_c) as y
  from weather_metrics
  where time between '2022-01-01' and '2023-01-02'
  and city_name = 'New York'
  group by 1
  order by 1;

percentile

with resume as (
  select time_bucket('1 month', time),
    percentile_agg(temp_c)
  from weather_metrics
  where time between '2022-01-01' and '2023-01-02'
    and city_name = 'New York'
  group by 1 order by 1
)
select 'p99' as name,
time_bucket as x,
approx_percentile(0.99, percentile_agg) as y
from resume
order by 2;

avg vs median - year bucket

with resume as (
  select time_bucket('1 month', time),
    percentile_agg(temp_c),
    avg(temp_c) as avg
  from weather_metrics
  where time between '2022-01-01' and '2023-01-02'
    and city_name = 'New York'
  group by 1 order by 1
),
median as (
  select 'median' as name,
  array_agg(time_bucket) as x,
  array_agg(approx_percentile(0.5, percentile_agg)) as y
  from resume
),
average as (
  select 'average' as name,
  array_agg(time_bucket) as x,
  array_agg(avg) as y
  from resume
)
SELECT * FROM median  UNION ALL
SELECT * FROM average ;

avg vs median - daily bucket

with resume as (
  select time_bucket('1 day', time),
    percentile_agg(temp_c),
    avg(temp_c) as avg
  from weather_metrics
  where time between '2022-01-01' and '2023-01-02'
    and city_name = 'New York'
  group by 1 order by 1
),
median as (
  select 'median' as name,
  array_agg(time_bucket) as x,
  array_agg(approx_percentile(0.5, percentile_agg)) as y
  from resume
),
average as (
  select 'average' as name,
  array_agg(time_bucket) as x,
  array_agg(avg) as y
  from resume
)
SELECT * FROM median  UNION ALL
SELECT * FROM average ;

uddsketch and tdigest

with resume as (
  select time_bucket('1 year', time),
    percentile_agg(temp_c),
    uddsketch(200, 0.001, temp_c),
    tdigest(200, temp_c),
    avg(temp_c)
  from weather_metrics
  where -- time between '2022-01-01' and '2023-01-02' and
  city_name = 'New York'
  group by 1 order by 1
),
median as (
  select 'median' as name,
  array_agg(time_bucket) as x,
  array_agg(approx_percentile(0.5, percentile_agg)) as y
  from resume
),
median_uddsketch as (
  select 'median uddsketch' as name,
  array_agg(time_bucket) as x,
  array_agg(approx_percentile(0.5, uddsketch)) as y
  from resume
),
median_tdigest as (
  select 'median uddsketch' as name,
  array_agg(time_bucket) as x,
  array_agg(approx_percentile(0.5, tdigest)) as y
  from resume
),
average as (
  select 'average' as name,
  array_agg(time_bucket) as x,
  array_agg(avg) as y
  from resume
)
SELECT * FROM median UNION ALL
SELECT * FROM median_uddsketch UNION ALL
SELECT * FROM median_tdigest UNION ALL
SELECT * FROM average ;

p1 and p99

with resume as (
  select time_bucket('1 month', time),
    percentile_agg(temp_c),
    avg(temp_c) as avg
  from weather_metrics
  where time between '2022-01-01' and '2023-01-02'
    and city_name = 'New York'
  group by 1 order by 1
),
p1 as (
  select 'p1' as name,
  array_agg(time_bucket) as x,
  array_agg(approx_percentile(0.01, percentile_agg)) as y
  from resume
),
p99 as (
  select 'p99' as name,
  array_agg(time_bucket) as x,
  array_agg(approx_percentile(0.99, percentile_agg)) as y
  from resume
),
median as (
  select 'median' as name,
  array_agg(time_bucket) as x,
  array_agg(approx_percentile(0.5, percentile_agg)) as y
  from resume
),
average as (
  select 'average' as name,
  array_agg(time_bucket) as x,
  array_agg(avg) as y
  from resume
)
SELECT * FROM median  UNION ALL
SELECT * FROM average UNION ALL
SELECT * FROM p1      UNION ALL
SELECT * FROM p99

lttb

with ny as (
  select (lttb(time, temp_c, 300) -> unnest()).* as pair 
  from weather_metrics
  where time between '2022-01-01' and '2023-01-01'
  and city_name = 'New York'
) select ny.time as x, ny.value as y from ny;

Exercises

Practical exercises to perform Time Series analysis in SQL and Timescaledb.

Practical exercises with the dataset

Hands-on exercises to perform Time Series analysis on the weather dataset.
Aggregating data with tookit.
Approximate Percentiles
Creating candlesticks (OHLC charts)
Correlation matrix: Creating a correlation matrix in Postgresql from the correlation coefficient
Downsampling: Using the LTTB function to reduce large datasets without losing visual similarity with the original data

Knowledge sharing

Presentations and knowledge sharing by the participants

Opportunities for students to present their findings and share their knowledge with the rest of the class.

Extra Resources

Thanks

@jonatasdp on {Twitter,Linkedin}
Github: @jonatas

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PGConf India 2023

Welcome

Timescaledb

&

Toolkit

Jônatas Davi Paganini

@jonatasdp

@jonatasdp

twitter: @jonatasdp

github: @jonatas

Agenda

Introduction

Data Science

Applications

Data

Interface

Create

Extension

Download

Table

Hypertable

Import

Timing

Counting

Approx. row count

granularity

Check

5WH

Explore

Questions

Time zone

Distinct

Count

Min / Max

Group

Avg

Time Series

Year bucket

Order by

Sum

Season

Views

Partitions

Explain

time bucket

Min / Max

OHLC

Stddev

array_agg

Toolkit

percentile_agg

Median from percentile

Pipeline

quartiles

CTE

Stats aggs

Average

Alias

Rollup

cascade

Variance

num vals

CAggs

caggs^2?

Candlesticks

View

Correlation

Correlation matrix

Combining pairs

Crosstab

Plotting

X & Y

type

title