Estimate Databento feed latency
Overview
In this example we will use the Live client to calculate latency of incoming records. We will use the ts_recv field to make a measurement of the time between Databento's servers receiving a message from the exchange, to the message being received by the client. To do this, we will request MBP-1 data for a parent symbol and calculate the latency for each message.
You can read our timestamping guide to learn more about our architecture.
Latency results will depend upon the quality of your system clock and internet connection. Your individual results might be very large or even negative given these factors. To obtain the most accurate results, we suggest you sync your clocks to our NTP service.
Example
import time import databento as db import matplotlib.pyplot as plt import numpy as np # Set parameters dataset = "GLBX.MDP3" product = "ES" num_samples = 5000 percentiles = [50, 90, 95, 99] num_bins = 100 # Create a live client live_client = db.Live(key="$YOUR_API_KEY") # Subscribe to MBP-1 data live_client.subscribe( dataset="GLBX.MDP3", schema="mbp-1", symbols=f"{product}.FUT", stype_in="parent", ) # Iterate over the live client and calculate latency latency_ms = [] for record in live_client: if isinstance(record, db.MBP1Msg): delta = time.time_ns() - record.ts_recv latency_ms.append(delta / 1e6) if len(latency_ms) >= num_samples: break fig, ax = plt.subplots(figsize=(10, 6)) x_min = min(latency_ms) * 0.95 x_max = max(latency_ms) * 1.05 # Create histogram with log bins log_bins = np.logspace(np.log10(x_min), np.log10(x_max), num_bins) n, bins, patches = ax.hist(latency_ms, bins=log_bins, rwidth=0.85, color="C3") y_max = max(n) * 1.25 percentile_handles = [] percentile_labels = [] # Create percentile lines for p in percentiles: p_value = np.percentile(latency_ms, p) line = ax.axvline(p_value, color="C0", linestyle="--", linewidth=1) ax.text(p_value, y_max * 0.9, f"{p}th", color="C0", rotation=-90) percentile_handles.append(line) percentile_labels.append(f"p{p} - {round(p_value, 2)} ms") # Set log scale and labels tick_locations = np.logspace(np.log10(x_min), np.log10(x_max), 10) ax.set_xscale("log") ax.set_xticks(tick_locations) ax.set_xticklabels([str(int(round(x))) for x in tick_locations]) ax.tick_params(axis="x", which="minor", bottom=False, labelbottom=False) # Set limits and labels ax.set_xlim(x_min * 0.95, x_max * 1.05) ax.set_ylim(0, y_max) ax.set_xlabel("Latency (ms)", fontsize=12) ax.set_ylabel("Frequency", fontsize=12) ax.set_title("Latency Distribution", fontsize=14) # Add legend and display ax.legend(handles=percentile_handles, labels=percentile_labels) plt.tight_layout() plt.show() 