Saturday, June 28 — Old Studies
What I studied/researched :
💬 Chat with GitHub Copilot
Click to expand the full conversation
**yashkrishh:** what does this do ?
**GitHub Copilot:** This Docker Compose file sets up a local development environment for data engineering work with two main services:
### Services
#### 1. PostgreSQL Database (`postgres`)
- **Image**: PostgreSQL version 14
- **Purpose**: Primary database server for data storage and analysis
- **Configuration**:
- Uses environment variables from `.env` file for database credentials
- Exposes database on a configurable host port (maps to container port 5432)
- Mounts the current directory to bootcamp inside the container
- Automatically initializes the database with:
- `data.dump` - Database dump file for initial data
- `init-db.sh` - Custom initialization script
- Persists data using a named volume (`postgres-data`)
#### 2. pgAdmin (`pgadmin`)
- **Image**: pgAdmin 4 web interface
- **Purpose**: Web-based PostgreSQL administration tool
- **Configuration**:
- Provides a GUI for managing the PostgreSQL database
- Accessible via web browser on configurable port
- Uses environment variables for admin credentials
- Persists configuration using a named volume (`pgadmin-data`)
### Key Features
- **Environment-based configuration**: All sensitive data (passwords, ports) stored in `.env` file
- **Data persistence**: Both database data and pgAdmin settings survive container restarts
- **Auto-restart**: Both services restart automatically if they fail
- **Database initialization**: Automatically loads initial data and runs setup scripts
This setup is commonly used for data engineering bootcamps or projects where you need a full PostgreSQL environment with a user-friendly admin interface.
---
**yashkrishh:** Error response from daemon: failed to set up container networking: driver failed programming external connectivity on endpoint my-postgres-container (3fbcc5f789e9be427b27832edaa132c87c2ae15913499a52ff32bf8abadf58df): failed to bind host port for 0.0.0.0:5432:172.21.0.3:5432/tcp: address already in use
make: *** [up] Error 1
**GitHub Copilot:** This error indicates that **port 5432 is already in use** on your Mac. Port 5432 is the default PostgreSQL port, and something else is already using it.
### Quick Solutions
#### Option 1: Check what's using port 5432
```bash
lsof -i :5432
```
If you see PostgreSQL already running, you can stop it:
```bash
# If installed via Homebrew
brew services stop postgresql
# Or kill the process directly (replace PID with actual process ID from lsof output)
kill -9
```
#### Option 2: Change the port in your .env file
Edit your `.env` file and change the `HOST_PORT` to a different port:
```bash
# ...existing code...
HOST_PORT=5433
# ...existing code...
```
#### Option 3: Stop all Docker containers using port 5432
```bash
# Check for running containers
docker ps
# Stop any PostgreSQL containers
docker stop $(docker ps -q --filter "expose=5432")
```
### Recommended Approach
I'd suggest **Option 2** (changing the port) as it's the safest. You can use port `5433` or any other available port. After changing the `.env` file, run:
```bash
make up
```
Then connect to your database using `localhost:5433` instead of `localhost:5432`.
---
**yashkrishh:** explain how cumulative dimensions, struct and array are used in this example
**GitHub Copilot:** This SQL query demonstrates advanced dimensional modeling techniques using **cumulative dimensions**, **structs**, and **arrays** to create a slowly changing dimension (SCD) for NBA players. Let me break down each concept:
#### Arrays Usage
The query uses arrays to store historical season statistics:
```sql
ARRAY_REMOVE(
ARRAY_AGG(
CASE
WHEN ps.season IS NOT NULL
THEN ROW(...)::season_stats
END)
OVER (PARTITION BY pas.player_name ORDER BY COALESCE(pas.season, ps.season)),
NULL
) AS seasons
```
- **Purpose**: Accumulates all historical seasons for each player into a single array
- **Benefit**: Avoids creating separate rows for each season; keeps all history in one record per player per year
#### Structs (Custom Types)
The `ROW(...)::season_stats` creates structured data:
```sql
ROW(
ps.season,
ps.gp, -- games played
ps.pts, -- points
ps.reb, -- rebounds
ps.ast -- assists
)::season_stats
```
- **Purpose**: Each array element is a structured record containing season statistics
- **Benefit**: Maintains data relationships and enables type safety
#### Cumulative Dimensions
This is the key concept - the query creates a **cumulative table** where:
```sql
WITH years AS (
SELECT * FROM GENERATE_SERIES(1996, 2022) AS season
), players_and_seasons AS (
SELECT *
FROM p
JOIN years y ON p.first_season <= y.season
)
```
##### How Cumulative Dimensions Work:
1. **Every Player Gets a Row for Every Year** (since their first season)
2. **Historical Data Accumulates**: The `seasons` array grows each year
3. **Current State Tracking**:
- `is_active`: Whether player played in current season
- `years_since_last_active`: How long since last game
- `scoring_class`: Classification based on most recent season
##### Example Output Structure:
```
Player: LeBron James, Season: 2022
├── Static Info: height, college, draft info
├── seasons: [
│ ├── {season: 2003, gp: 79, pts: 20.9, ...}
│ ├── {season: 2004, gp: 80, pts: 27.2, ...}
│ └── ... (all seasons through 2022)
│ ]
├── scoring_class: 'star' (based on latest season)
├── years_since_last_active: 0
└── is_active: true
```
##### Benefits of This Approach:
1. **Time Travel Queries**: Can analyze any player's state at any point in time
2. **Trend Analysis**: Easy to calculate career progressions and patterns
3. **Storage Efficiency**: One row per player per year vs. separate fact tables
4. **Query Performance**: No complex joins needed for historical analysis
This pattern is particularly powerful for analytics where you need both current state and full historical context in a single query.
---
**yashkrishh:** can you explain this query slowly from the beginning
**GitHub Copilot:** I'll walk through this query step by step to show how it builds a cumulative dimensional table for NBA players.
#### Step 1: Create Year Range (CTE `years`)
```sql
WITH years AS (
SELECT *
FROM GENERATE_SERIES(1996, 2022) AS season
)
```
- Creates a table with every year from 1996 to 2022
- Each row represents one NBA season
- Result: `{1996, 1997, 1998, ..., 2022}`
#### Step 2: Find Each Player's Career Start (CTE `p`)
```sql
p AS (
SELECT
player_name,
MIN(season) AS first_season
FROM player_seasons
GROUP BY player_name
)
```
- For each player, finds their **first NBA season**
- Groups all seasons by player and takes the minimum year
- Example result:
```
LeBron James → 2003
Kobe Bryant → 1996
```
#### Step 3: Create Player-Season Grid (CTE `players_and_seasons`)
```sql
players_and_seasons AS (
SELECT *
FROM p
JOIN years y
ON p.first_season <= y.season
)
```
- **Cartesian product** with a filter
- Each player gets a row for **every season since their career started**
- Example: LeBron (started 2003) gets rows for 2003, 2004, 2005, ..., 2022
- This creates the "spine" of our cumulative table
#### Step 4: Build Cumulative Season Arrays (CTE `windowed`)
```sql
windowed AS (
SELECT
pas.player_name,
pas.season,
ARRAY_REMOVE(
ARRAY_AGG(
CASE
WHEN ps.season IS NOT NULL
THEN ROW(ps.season, ps.gp, ps.pts, ps.reb, ps.ast)::season_stats
END)
OVER (PARTITION BY pas.player_name ORDER BY COALESCE(pas.season, ps.season)),
NULL
) AS seasons
FROM players_and_seasons pas
LEFT JOIN player_seasons ps
ON pas.player_name = ps.player_name
AND pas.season = ps.season
)
```
##### Breaking down the window function:
- **LEFT JOIN**: Matches actual season data when available
- **WINDOW FUNCTION**: `ARRAY_AGG(...) OVER (PARTITION BY player_name ORDER BY season)`
- **Accumulation**: For each row, collects ALL previous seasons into an array
- **ROW()::season_stats**: Creates structured data for each season
##### Example progression for LeBron:
```
2003: seasons = [{2003: 79gp, 20.9pts, ...}]
2004: seasons = [{2003: 79gp, 20.9pts, ...}, {2004: 80gp, 27.2pts, ...}]
2005: seasons = [{2003: 79gp, 20.9pts, ...}, {2004: 80gp, 27.2pts, ...}, {2005: 82gp, 31.4pts, ...}]
```
#### Step 5: Collect Static Player Info (CTE `static`)
```sql
static AS (
SELECT
player_name,
MAX(height) AS height,
MAX(college) AS college,
MAX(country) AS country,
MAX(draft_year) AS draft_year,
MAX(draft_round) AS draft_round,
MAX(draft_number) AS draft_number
FROM player_seasons
GROUP BY player_name
)
```
- Gathers **unchanging** player attributes
- Uses MAX() to handle any inconsistencies in source data
- These attributes don't change over time (height, draft info, etc.)
#### Step 6: Final Assembly and Calculations
```sql
SELECT
w.player_name,
s.height, s.college, s.country, s.draft_year, s.draft_round, s.draft_number,
seasons AS season_stats,
-- Classification based on MOST RECENT season
CASE
WHEN (seasons[CARDINALITY(seasons)]::season_stats).pts > 20 THEN 'star'
WHEN (seasons[CARDINALITY(seasons)]::season_stats).pts > 15 THEN 'good'
WHEN (seasons[CARDINALITY(seasons)]::season_stats).pts > 10 THEN 'average'
ELSE 'bad'
END::scoring_class AS scoring_class,
-- Years since last active season
w.season - (seasons[CARDINALITY(seasons)]::season_stats).season as years_since_last_active,
w.season,
-- Is player active THIS season?
(seasons[CARDINALITY(seasons)]::season_stats).season = season AS is_active
```
##### Key calculations explained:
- **`seasons[CARDINALITY(seasons)]`**: Gets the LAST element in the seasons array (most recent season played)
- **`scoring_class`**: Classifies player based on their most recent season's scoring
- **`years_since_last_active`**: Current season minus last season played
- **`is_active`**: True if player played in the current season
##### Final Result Structure
Each row represents a player's **cumulative state** at a specific point in time:
```
LeBron James, 2022:
├── Static: height=6'9", college=None, draft_year=2003
├── season_stats: [{2003 stats}, {2004 stats}, ..., {2022 stats}]
├── scoring_class: 'star' (based on 2022 performance)
├── years_since_last_active: 0 (he played in 2022)
├── season: 2022
└── is_active: true
```
This creates a **slowly changing dimension** where you can query any player's complete history and current state at any point in their career timeline.
---
**yashkrishh:** explain what is slowly changing dimensions wrt this query
**GitHub Copilot:** This query demonstrates **Slowly Changing Dimensions (SCD) Type 2** by tracking historical changes in player classifications over time. Let me explain the concept and how this query implements it.
#### What are Slowly Changing Dimensions?
**Slowly Changing Dimensions** handle attributes that change over time but not frequently. There are different types:
- **Type 1**: Overwrite old values (no history)
- **Type 2**: Keep full history with date ranges
- **Type 3**: Keep limited history (current + previous)
This query implements **Type 2 SCD** for the `scoring_class` attribute.
#### Step-by-Step Breakdown
##### Step 1: Detect Changes (`streak_started`)
```sql
WITH streak_started AS (
SELECT player_name,
current_season,
scoring_class,
LAG(scoring_class, 1) OVER
(PARTITION BY player_name ORDER BY current_season) <> scoring_class
OR LAG(scoring_class, 1) OVER
(PARTITION BY player_name ORDER BY current_season) IS NULL
AS did_change
FROM players
)
```
**Purpose**: Identifies when a player's `scoring_class` changes
- **LAG()**: Looks at previous season's scoring class
- **did_change = TRUE** when:
- Current scoring_class ≠ previous_scoring_class
- First record for player (LAG is NULL)
**Example for LeBron James**:
```
Season | scoring_class | Previous | did_change
2003 | good | NULL | TRUE
2004 | star | good | TRUE
2005 | star | star | FALSE
2006 | star | star | FALSE
2007 | good | star | TRUE
```
##### Step 2: Create Streak Identifiers (`streak_identified`)
```sql
streak_identified AS (
SELECT
player_name,
scoring_class,
current_season,
SUM(CASE WHEN did_change THEN 1 ELSE 0 END)
OVER (PARTITION BY player_name ORDER BY current_season) as streak_identifier
FROM streak_started
)
```
**Purpose**: Groups consecutive seasons with same scoring_class
- **Running SUM**: Increments only when `did_change = TRUE`
- Creates unique identifier for each "streak" of same classification
**Example continuation**:
```
Season | scoring_class | did_change | streak_identifier
2003 | good | TRUE | 1
2004 | star | TRUE | 2
2005 | star | FALSE | 2
2006 | star | FALSE | 2
2007 | good | TRUE | 3
```
##### Step 3: Aggregate Streaks (`aggregated`)
```sql
aggregated AS (
SELECT
player_name,
scoring_class,
streak_identifier,
MIN(current_season) AS start_date,
MAX(current_season) AS end_date
FROM streak_identified
GROUP BY 1,2,3
)
```
**Purpose**: Collapses each streak into date ranges
- **MIN(current_season)**: When this classification period started
- **MAX(current_season)**: When this classification period ended
**Final Example Result**:
```
player_name | scoring_class | start_date | end_date
LeBron James | good | 2003 | 2003
LeBron James | star | 2004 | 2006
LeBron James | good | 2007 | 2007
```
#### SCD Type 2 Benefits
This approach provides:
1. **Complete History**: Can see exactly when classifications changed
2. **Point-in-Time Queries**: "What was LeBron's classification in 2005?" → "star"
3. **Trend Analysis**: How long do players typically stay in each classification?
4. **Data Integrity**: Never lose historical context
#### Real-World SCD Example
Imagine this for customer data:
```
Customer | Status | start_date | end_date
John | Bronze | 2020-01-01 | 2020-12-31
John | Silver | 2021-01-01 | 2022-06-30
John | Gold | 2022-07-01 | 9999-12-31 -- Current
```
This query transforms the cumulative dimension (one row per player per season) into an SCD Type 2 format (one row per classification period), making it easier to analyze transitions and durations of different states.
---
**yashkrishh:** similarly explain all the concepts used in this query
**GitHub Copilot:** This query creates a **graph data structure** by building **edges** between NBA players based on their game interactions. Let me explain all the key concepts:
#### Graph Data Modeling
This query transforms relational game data into a **graph structure**:
- **Nodes**: Players
- **Edges**: Relationships between players (teammates or opponents)
- **Edge Properties**: Statistics about their interactions
#### Step 1: Data Deduplication (`deduped`)
```sql
WITH deduped AS (
SELECT *, row_number() over (PARTITION BY player_id, game_id) AS row_num
FROM game_details
)
```
**Purpose**: Removes duplicate records for same player in same game
- **ROW_NUMBER()**: Assigns sequential numbers within each partition
- **PARTITION BY player_id, game_id**: Groups by unique player-game combinations
- **Why needed**: Source data might have duplicate entries due to data quality issues
#### Step 2: Filter to Unique Records (`filtered`)
```sql
filtered AS (
SELECT * FROM deduped
WHERE row_num = 1
)
```
**Purpose**: Keeps only the first occurrence of each player-game combination
- **row_num = 1**: Takes the first record from each partition
- **Result**: Clean dataset with one record per player per game
#### Step 3: Self-Join for Player Pairs (`aggregated`)
```sql
FROM filtered f1
JOIN filtered f2
ON f1.game_id = f2.game_id
AND f1.player_name <> f2.player_name
WHERE f1.player_id > f2.player_id
```
##### Self-Join Concepts:
**Same Table Twice**:
- **f1**: First player (left side of edge)
- **f2**: Second player (right side of edge)
**Join Conditions**:
- **`f1.game_id = f2.game_id`**: Both players in same game
- **`f1.player_name <> f2.player_name`**: Different players
- **`f1.player_id > f2.player_id`**: **Prevents duplicate edges**
##### Duplicate Prevention Logic:
Without the `f1.player_id > f2.player_id` condition, you'd get:
```
LeBron → Kobe
Kobe → LeBron (duplicate relationship)
```
With the condition, you only get:
```
LeBron → Kobe (assuming LeBron's ID > Kobe's ID)
```
#### Edge Type Classification
```sql
CASE WHEN f1.team_abbreviation = f2.team_abbreviation
THEN 'shares_team'::edge_type
ELSE 'plays_against'::edge_type
END
```
**Custom Enum Type**: `edge_type` (defined elsewhere)
- **'shares_team'**: Players on same team (teammates)
- **'plays_against'**: Players on different teams (opponents)
**Purpose**: Categorizes the nature of player relationships
#### Aggregation Metrics
```sql
COUNT(1) AS num_games,
SUM(f1.pts) AS left_points,
SUM(f2.pts) as right_points
```
**Edge Properties** (statistics about the relationship):
- **num_games**: How many games these players appeared in together
- **left_points**: Total points scored by first player in their shared games
- **right_points**: Total points scored by second player in their shared games
##### Complete Example Output
```sql
player_id | player_name | player_id | player_name | edge_type | num_games | left_points | right_points
23 | LeBron James | 24 | Kobe Bryant | plays_against | 22 | 580 | 612
23 | LeBron James | 6 | Dwyane Wade | shares_team | 224 | 5890 | 4420
```
#### Graph Analysis Applications
This edge table enables powerful graph analytics:
##### Network Analysis:
```sql
-- Find players with most connections
SELECT player_name, COUNT(*) as connections
FROM player_edges
GROUP BY player_name
ORDER BY connections DESC;
```
##### Teammate Analysis:
```sql
-- Find strongest teammate partnerships
SELECT left_player, right_player, num_games
FROM player_edges
WHERE edge_type = 'shares_team'
ORDER BY num_games DESC;
```
##### Rivalry Analysis:
```sql
-- Find biggest rivalries (most games against each other)
SELECT left_player, right_player, num_games
FROM player_edges
WHERE edge_type = 'plays_against'
ORDER BY num_games DESC;
```
#### Key Graph Concepts Demonstrated
1. **Undirected Edges**: Using `player_id > player_id` creates undirected relationships
2. **Edge Properties**: Storing statistics about relationships, not just connections
3. **Edge Types**: Categorizing different kinds of relationships
4. **Graph from Relational**: Converting traditional relational data into graph structure
5. **Deduplication**: Essential preprocessing for graph accuracy
This pattern is fundamental in **network analysis**, **recommendation systems**, and **social graph modeling** where relationships between entities are as important as the entities themselves.
---
**yashkrishh:** @workspace /explain Write an explanation for the active selection as paragraphs of text.
```oracle-sql
SELECT
pas.player_name,
pas.season,
ARRAY_REMOVE(
ARRAY_AGG(
CASE
WHEN ps.season IS NOT NULL
THEN ROW(
ps.season,
ps.gp,
ps.pts,
ps.reb,
ps.ast
)::season_stats
END)
OVER (PARTITION BY pas.player_name ORDER BY COALESCE(pas.season, ps.season)),
NULL
) AS seasons
FROM players_and_seasons pas
LEFT JOIN player_seasons ps
ON pas.player_name = ps.player_name
AND pas.season = ps.season
ORDER BY pas.player_name, pas.season
```
Workspace: This SQL query is the core logic of the `windowed` CTE that builds a comprehensive history of each player's statistics across all seasons. It's a sophisticated example of using window functions with arrays to create a cumulative statistical record.
The query starts by selecting from `players_and_seasons` (which contains every player for every season from their first season through 2022) and left joins with `player_seasons` (which only contains seasons where players actually played). This join pattern ensures that even seasons where a player didn't play are represented in the final result.
The most complex part is the `ARRAY_AGG` window function that builds an array of season statistics for each player. It uses `PARTITION BY pas.player_name` to group records by player, and `ORDER BY COALESCE(pas.season, ps.season)` to arrange seasons chronologically. The `CASE` statement only creates a `season_stats` row when `ps.season IS NOT NULL`, meaning the player actually played that season. When a player didn't play, the case returns NULL.
The `ROW()::season_stats` construct creates a composite type containing the season year, games played, points, rebounds, and assists. This is cast to a custom `season_stats` type, which suggests the database has a predefined composite type for storing these statistics.
The `ARRAY_REMOVE(..., NULL)` function is crucial here - it strips out all the NULL values that would otherwise appear in the array for seasons when players didn't play. This creates a clean array containing only the seasons with actual statistical data.
**Key gotcha**: The window function creates the same cumulative array for every row of the same player, which means you'll see the complete statistical history repeated for each season row. This is intentional for the later processing steps, but it's important to understand that this intermediate result has significant data duplication that gets resolved in the final query.
---
**yashkrishh:** The most complex part is the ARRAY_AGG window function that builds an array of season statistics for each player. It uses PARTITION BY pas.player_name to group records by player, and ORDER BY COALESCE(pas.season, ps.season) to arrange seasons chronologically. The CASE statement only creates a season_stats row when ps.season IS NOT NULL, meaning the player actually played that season. When a player didn't play, the case returns NULL.
The ROW()::season_stats construct creates a composite type containing the season year, games played, points, rebounds, and assists. This is cast to a custom season_stats type, which suggests the database has a predefined composite type for storing these statistics.
The ARRAY_REMOVE(..., NULL) function is crucial here - it strips out all the NULL values that would otherwise appear in the array for seasons when players didn't play. This creates a clean array containing only the seasons with actual statistical data.
---
**yashkrishh:** can you give me a small easy example to visualise the row function, array_agg, the window function and the array_Remove function described here :
**GitHub Copilot:** Here's a simple example to visualize how these functions work together:
#### Sample Data Setup
Let's say we have this simplified data:
**players_and_seasons** (every player gets a row for every season since they started):
```
player_name | season
LeBron | 2003
LeBron | 2004
LeBron | 2005
Kobe | 2004
Kobe | 2005
```
**player_seasons** (only seasons where they actually played):
```
player_name | season | pts
LeBron | 2003 | 20
LeBron | 2005 | 27 -- Missing 2004 (injured)
Kobe | 2004 | 24
Kobe | 2005 | 22
```
#### Step-by-Step Function Breakdown
1. **After LEFT JOIN**
```
player_name | pas.season | ps.season | pts
LeBron | 2003 | 2003 | 20
LeBron | 2004 | NULL | NULL -- No match (didn't play)
LeBron | 2005 | 2005 | 27
Kobe | 2004 | 2004 | 24
Kobe | 2005 | 2005 | 22
```
2. **ROW() Function Creates Structs**
```sql
CASE WHEN ps.season IS NOT NULL
THEN ROW(ps.season, ps.pts)::season_stats
END
```
For each row:
- LeBron 2003: `ROW(2003, 20)::season_stats` → `{season: 2003, pts: 20}`
- LeBron 2004: `NULL` (because ps.season IS NULL)
- LeBron 2005: `ROW(2005, 27)::season_stats` → `{season: 2005, pts: 27}`
3. **ARRAY_AGG Window Function (Before ARRAY_REMOVE)**
```sql
ARRAY_AGG(...) OVER (PARTITION BY player_name ORDER BY season)
```
LeBron's rows (accumulating as we go):
```
Row 1 (2003): [{season: 2003, pts: 20}]
Row 2 (2004): [{season: 2003, pts: 20}, NULL]
Row 3 (2005): [{season: 2003, pts: 20}, NULL, {season: 2005, pts: 27}]
```
Kobe's rows:
```
Row 1 (2004): [{season: 2004, pts: 24}]
Row 2 (2005): [{season: 2004, pts: 24}, {season: 2005, pts: 22}]
```
4. **ARRAY_REMOVE Function Cleans Up**
```sql
ARRAY_REMOVE(..., NULL)
```
Final result after removing NULLs:
LeBron's rows:
```
Row 1 (2003): [{season: 2003, pts: 20}]
Row 2 (2004): [{season: 2003, pts: 20}] -- NULL removed
Row 3 (2005): [{season: 2003, pts: 20}, {season: 2005, pts: 27}] -- NULL removed
```
Kobe's rows (no change, no NULLs):
```
Row 1 (2004): [{season: 2004, pts: 24}]
Row 2 (2005): [{season: 2004, pts: 24}, {season: 2005, pts: 22}]
```
#### Final Query Result
```
player_name | season | seasons_array
LeBron | 2003 | [{season: 2003, pts: 20}]
LeBron | 2004 | [{season: 2003, pts: 20}]
LeBron | 2005 | [{season: 2003, pts: 20}, {season: 2005, pts: 27}]
Kobe | 2004 | [{season: 2004, pts: 24}]
Kobe | 2005 | [{season: 2004, pts: 24}, {season: 2005, pts: 22}]
```
**Key Insight**: Notice how LeBron's 2004 row still shows his cumulative history (just 2003), even though he didn't play in 2004. This is how we maintain a complete timeline while only storing actual performance data.
</details>