When you look at a chart on Sahi and see prices move in real time, a lot is happening behind the scenes.
Not just “getting data” – but moving, cleaning, distributing, summarizing, storing, and showing massive amounts of market information in a way that feels effortless.
Let’s walk through this journey in simple terms.
1) Getting Data from the Exchange – The Fastest Way Possible
Sahi receives market data directly from stock exchanges through dedicated fiber lines.
Think of this as a private highway instead of public roads.
- No internet congestion
- No middlemen
- No delays from third parties
As soon as a trade happens on the exchange, Sahi gets that update almost instantly.
2) Making All Data Speak the Same Language
Different exchanges send data in different formats.
Sahi runs small programs (called adaptors) that:
- Read raw exchange data
- Convert it into a common internal format
So downstream systems don’t waste time figuring out “what exchange sent this” — everything looks identical.
This saves precious milliseconds.
3) Two Parallel Paths: Live Prices & Historical Candles
Once normalized, the data splits into two paths:
A) Live Tick Path (For Instant Updates)
Used for:
- Last traded price
- Real-time chart movement
- Live quotes
These updates go straight to Sahi’s feed servers and then to your app over WebSocket.
No database in between.
Why?
Because writing every single tick to a database first would slow things down.
This is why live charts on Sahi feel “alive”.
B) Aggregated Data Path (For Candles & History)
Raw ticks are also sent to another system that:
- Groups thousands of trades
- Builds candles like:
- 5-second
- 1-minute
- 5-minute
- 1-hour
Instead of storing every trade, Sahi stores summaries:
Open, High, Low, Close, Volume
These summaries are what most charts actually use.
4) Why Sahi Stores Candles Instead of Raw Ticks
Imagine this:
- A liquid stock can generate millions of trades per day
- Storing all of them forever is expensive and unnecessary
Most traders care about:
- Price movement over time
- Candles
- Trends
So Sahi stores:
✔ Aggregated candles
❌ Not every raw tick
This keeps the system fast and efficient.
5) Why Sahi Uses ScyllaDB
ScyllaDB is the database used to store candle data.
In simple words, Sahi chose ScyllaDB because it is:
Extremely Fast
It can handle millions of writes per second.
Perfect for constantly receiving fresh candles.
Built for Time-Series Data
Candles are time-based (every minute, every hour, etc.).
ScyllaDB is designed for exactly this type of workload.
Scales Easily
If data grows:
- Add more servers
- Capacity increases linearly
No need for painful migrations.
Always Available
Data is automatically replicated across machines.
If one server fails, others keep serving.
6) How Sahi Handles Huge Amounts of Data Without Slowing Down
Instead of putting all history of a stock in one giant bucket, Sahi:
- Breaks data into time-based chunks
- Example:
- One chunk for Jan-Mar
- Another for Apr-Jun
So when you open a chart:
- Only the relevant chunks are read
- Not the entire lifetime of the stock
This keeps chart loading fast even for decades of data.
7) What Happens When You Open a Chart
- App asks for historical candles
- Candles API fetches from ScyllaDB
- Data is sent back in milliseconds
- Live ticks start streaming
- Chart updates instantly
To you, it feels seamless.
8) Why This Architecture Matters for Traders
Because speed changes outcomes.
- Faster charts → better entries
- No lag → no stale prices
- Smooth scrolling → better analysis
Instead of relying on generic third-party systems, Sahi built everything specifically for trading speed.
In One Sentence
Sahi streams live prices directly to your screen for zero delay, stores only smartly aggregated candle data in ScyllaDB for lightning-fast history, and designs every layer to remove unnecessary waiting.
That’s why when markets move fast, Sahi keeps up.
