October 2020 Summaries

QuestDB, an open-source time-series database designed for high-demand environments, has enhanced its capabilities by integrating authentication for the InfluxDB line protocol, which is widely used due to its simplicity and minimal overhead. The protocol allows data to be sent line by line via UDP or TCP, but previously lacked robust security measures. To address this, QuestDB introduced a challenge/response authentication mechanism using elliptic curve cryptography (ECC curve P-256) with SHA-256 for strong, future-proof security. This method allows QuestDB to be configured with only public cryptographic keys, reducing the risk of storing sensitive data. The authentication process involves the client sending a key ID to the server, which responds with a nonce that the client signs and returns, ensuring that only authorized clients can send data. This development allows users with specific security needs to implement authentication without the complexity of full encryption, maintaining the efficiency and portability of QuestDB while keeping endpoints secure.

QuestDB, an open-source time-series database, was used to analyze the economic impact of the antiquated taxi meter system on NYC cab drivers, contrasting it with factors like competition from Uber and Lyft. The meter system calculates fare based on speed, distance, and time, with a guaranteed $30 per hour and an optional component based on speed above 12mph, similar to financial options pricing. The study found that over the past decade, the average speed of NYC taxis declined significantly, reducing the optional fare component and resulting in substantial financial losses for drivers. This decline in speed is attributed to increased traffic, urban planning changes, and competition from ride-sharing services, leading to an estimated loss of up to $10 per hour per driver, or $1.2 billion annually for the industry. The base fare of $2.50 is intended as an incentive, but its effectiveness depends on the waiting time between rides, which has likely increased due to competition. The study suggests that if drivers are uncertain about finding the next passenger, it might be more economically viable to drive slowly. The dataset used for this analysis is available for public exploration on QuestDB's demo server, inviting further research into factors like weather impacts on fares and traffic conditions.