Tracking multiple cryptocurrency exchanges using a time series database

A candlestick chart showing Bitcoin prices in US Dollars
Photo by Nick Chong via Unsplash

This submission comes from one of our community contributors Yitaek Hwang who has put together another excellent tutorial that shows how to stream blockchain data into QuestDB for time series data visualization and analysis.

Thanks for another great contribution, Yitaek!

Tracking volatility using a Bitcoin ticker#

Crypto investors saw 1T USD wiped out of the market in May, as the price of Bitcoin and other cryptocurrencies nosedived to their lowest in 2021. Given the volatile nature of this asset class, traders looking to navigate the market need fast and reliable price data from multiple exchanges. Earlier this year, I shared a way to analyze cryptocurrency price trends in real-time via Kafka and QuestDB. That project polled the Coinbase API for the latest price and calculated moving averages prior to sending it to a time series database for further analysis.

Utilizing Kafka, I was able to leverage its scalability and ordering guarantees prior to ingesting in QuestDB. However, since I was using the PostgreSQL wire protocol interface, it was not optimized for fast ingestion. Also, the price feed was limited to a single exchange (i.e. Coinbase Pro), reducing our ability to react quickly to volatility such as buying or selling Bitcoin based on best price on another exchange.

Fortunately, QuestDB team released an update with significant re-write of their InfluxDB Line Protocol for fast ingestion and support for out-of-order data. So I decided to modify my previous project to test out these features.

Disclaimer: This tutorial is not investment or financial advice. All views expressed here are my own.


Note: Memory can be increased on Docker Desktop in Settings -> Resources -> Memory and increasing the default limit from 2GB to 4GB.

Out-of-order data ingestion#

With the release of v6.0.0, QuestDB no longer enforces that timeseries data is sent in strict chronological order. This means that QuestDB can deal with out-of-order data caused by network delays, hardware jitter, or different delivery mechanisms, using the timestamp field instead of relying on the received time. As shown in the example below, if a message is received by QuestDB after the sample time (delay of 9s, 1s, 2s respectively), QuestDB can recognize this lag and re-orders them prior to committing them to the database.

A diagram illustrating how delays and network jitter can cause records to arrive out-of-order by time at a database
Data arriving out-of-order due to network delays or jitter

This removes the burden from the programmer to design their ingestion mechanism to deal with delays or network skew. While Kafka guarantees ordering within a partition, it was still my responsibility to send messages in order to the broker to consume them chronologically. Since I was polling a single endpoint, there was little chance of records being sent out-of-order. But by adding new exchanges to poll information from (e.g. Binance, Gemini), API response times and network delays come into factor.

InfluxDB Line Protocol compatibility#

QuestDB also recommends using the InfluxDB line protocol as the primary ingestion method for highest performance. In fact, QuestDB team achieved write speeds of 1.43M rows/sec using an AMD Ryzen5 when running the Time Series Benchmark Suite.

InfluxDB line protocol also provides the following benefits over using the Postgres client (as used in the Kafka project via Kafka Connect):

  • higher-throughput
  • robust ingestion from multiple sources into tables
  • configurable commit-lag for out-of-order data
  • schema-agnostic ingestion (i.e. adding new fields will dynamically create new columns)

The message format for InfluxDB looks as follows:

table_name,<tag>=<value> <label>=<value>,<label>=<value> timestamp\n

Each line of the InfluxDB record is ingested as a new row in QuestDB. For this project, table_name was crypto, tag is the symbol for the cryptocurrency (e.g. BTC) with other labels including columns for exchange names and prices.

Writing data from multiple cryptocurrency exchanges#

To start, first pull the latest version of QuestDB (v6.0.3 or higher):

docker pull questdb/questdb:latest

Run the image with the following command:

docker run -p 9000:9000 -p 9009:9009 questdb/questdb

Port 9009 is used for sending new data to QuestDB using InfluxDB line protocol, and the web interface for QuestDB is exposed on port 9000 to explore data and run SQL queries.

For the purposes of the demo, I’ve decided to pull the price of Bitcoin from Binance and Gemini every second. To test the out-of-order ingestion more obvious, I purposely introduced a random delay between 1–5s before sending price data from Gemini.

We do not need to create tables beforehand when using InfluxDB line protocol, so we can directly run the following script and a table crypto will be created on the fly:

const axios = require("axios")
const net = require("net")
const influxClient = new net.Socket()
const HOST = "localhost"
const PORT = 9009
function randomInt(low, high) {
return Math.floor(Math.random() * (high - low) + low)
async function main() {
await influxClient.connect(PORT, HOST)
async function getBinanceData() {
const { data } = await axios.get(
const row = `crypto,currency=BTC,exchange=Binance price=${data.price} ${ * 1e6
await influxClient.write(`${row}\n`)
setTimeout(getBinanceData, 1000)
async function getGeminiData() {
const { data } = await axios.get("")
const { price } = data.find((i) => i.pair === "BTCUSD")
const row = `crypto,currency=BTC,exchange=Gemini price=${price} ${ * 1e6
setTimeout(async () => {
await influxClient.write(`${row}\n`)
}, randomInt(1000, 5000))
setTimeout(getGeminiData, 1000)

To check if the data is committed in chronological order, I can run the isOrdered function in a query:

SELECT isOrdered(timestamp) is_ts_ordered FROM crypto

And despite the fact that I introduced a random network delay on the Gemini price feed, you can see that Bitcoin price from the two exchanges are committed in order.


In a more realistic scenario, we would run different services on the cloud to pull data from various exchanges without intentional network delays. Once we figure out a pattern in terms of API response times or lags, we can optimize this process even further by tweaking the commit lag variables on QuestDB.

Specifying this known lag interval can reduce the compute resources QuestDB uses to sort out of order data then merging with persisted data to achieve higher throughput:

# via env variables
docker run -p 8812:8812 -p 9000:9000 -p 9009:9009 \
-e QDB_CAIRO_COMMIT_LAG=20000 questdb/questdb
-- via SQL
ALTER TABLE crypto SET PARAM maxUncommittedRows = 10000

For a project requiring high-throughput and performance needs, QuestDB provides nice features to enjoy the best of both worlds: fast ingestion via InfluxDB line protocol without worrying about data being out or order with full SQL support to analyze and act on that data. As with the Kafka project, feel free to connect it with your account to execute trades with the price data or build out more sophisticated statistical models.

If you like this content, we'd love to know your thoughts! Feel free to share your feedback or come and say hello in the QuestDB Community Slack.

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