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Demonstrating ArangoDB VelocyPack: A High-Performance Binary Data Format


After my last post, I received requests for more how-tos and technical posts. I apologize for the delayed response after my recent deluge of articles. It took me a bit to come up with something I wanted to write about. This is one of my favorite libraries, but it doesn't get the love it deserves.


In modern databases, efficient data serialization and deserialization are paramount to achieving high performance. ArangoDB, a multi-model database, addresses this need with its innovative binary data format, VelocyPack. This article delves into the intricacies of VelocyPack, demonstrating its advantages, usage, and how it enhances the performance of ArangoDB with code examples in Java and Rust.

What is VelocyPack?

VelocyPack is a compact, fast, and efficient binary data format developed by ArangoDB. It is designed to serialize and deserialize data quickly, minimizing the overhead associated with data storage and transmission. VelocyPack is similar to JSON in its capability to represent complex data structures, but it surpasses JSON in performance due to its binary nature (ArangoDB, 2022).

Key Features of VelocyPack

  1. Compactness: VelocyPack's binary format ensures data is stored compactly, reducing storage space and improving cache efficiency.
  2. Speed: The binary nature of VelocyPack allows for faster serialization and deserialization compared to text-based formats like JSON.
  3. Flexibility: VelocyPack can represent various data types, including nested objects and arrays, similar to JSON.
  4. Schema-free: Like JSON, VelocyPack is schema-free, allowing for dynamic data structures (ArangoDB, 2022).

Advantages of VelocyPack in ArangoDB

Performance Boost

One of the primary advantages of VelocyPack is its performance. Binary formats are inherently faster for both serialization and deserialization compared to text-based formats like JSON and XML. While JSON is widely used due to its simplicity and human-readable format, it is not as efficient in terms of speed and space. XML, another alternative, offers robust data structure representation but at the cost of verbosity and slower processing. VelocyPack's compact binary format ensures minimal overhead, making it much faster and more efficient (Stonebraker & Cattell, 2017).

Reduced Storage Footprint

VelocyPack's compact representation of data reduces the storage footprint. This reduction is especially beneficial for large datasets, where the savings in storage space can translate to significant cost reductions and performance improvements in data retrieval. Compared to formats like BSON (Binary JSON used in MongoDB), VelocyPack is more space-efficient, providing better performance (ArangoDB, 2022).

Efficient Data Transmission

The compact nature of VelocyPack also benefits data transmission over networks. Smaller data sizes mean less bandwidth usage and faster transmission times, essential for distributed databases and applications that rely on real-time data. Protocol Buffers (Protobuf) by Google is another binary format with similar advantages; VelocyPack's integration with ArangoDB offers seamless usage within this specific database environment (Schöni, 2019).

How VelocyPack Works

Data Structure

VelocyPack represents data using a binary format that includes type information and the data itself. This approach allows VelocyPack to handle many data types, including integers, floating-point numbers, strings, arrays, and objects. Each data type is encoded using a specific format that optimizes space and speed (ArangoDB, 2022).

Serialization and Deserialization

The process of converting data to VelocyPack format (serialization) and converting it back to its original form (deserialization) is highly optimized. VelocyPack includes efficient algorithms for both operations, ensuring minimal overhead. The following sections demonstrate how to serialize and deserialize data using VelocyPack in Java and Rust.

Using VelocyPack in ArangoDB


Before diving into examples, ensure that ArangoDB is installed on your system. You can download and install ArangoDB from the official website. Once installed, you can use the ArangoDB shell (arangosh) or one of the supported drivers to interact with the database.

Serialization Example in Java

import com.arangodb.velocypack.VPack;
import com.arangodb.velocypack.VPackParser;
import com.arangodb.velocypack.exception.VPackException;

import java.util.HashMap;
import java.util.Map;

public class VelocyPackExample {
public static void main(String[] args) {
VPack vpack = new VPack.Builder().build();

        Map<String, Object> jsonObject = new HashMap<>();
        jsonObject.put("name", "Alice");
        jsonObject.put("age", 30);
        jsonObject.put("city", "Wonderland");
        jsonObject.put("interests", new String[]{"reading", "gardening", "biking"});

        try {
            byte[] serializedData = vpack.serialize(jsonObject);
            System.out.println("Serialized data: " + serializedData);

            Map<String, Object> deserializedData = vpack.deserialize(serializedData, Map.class);
            System.out.println("Deserialized data: " + deserializedData);
        } catch (VPackException e) {

Serialization Example in Rust

NOTE: Example in Rust by special request!

use serde::{Serialize, Deserialize};
use velocypack::to_vec;
use velocypack::from_slice;

#[derive(Serialize, Deserialize, Debug)]
struct Person {
    name: String,
    age: u32,
    city: String,
    interests: Vec<String>,

fn main() {
    let person = Person {
        name: "Alice" .to_string(),
        age: 30,
        city: "Wonderland" .to_string(),
        interests: vec!["reading".to_string(), "gardening".to_string(), "biking".to_string()],

    // Serialize to VelocyPack
    let serialized_data = to_vec(&person).unwrap();
    println!("Serialized data: {:?}", serialized_data);

    // Deserialize from VelocyPack
    let deserialized_data: Person = from_slice(&serialized_data).unwrap();
    println!("Deserialized data: {:?}", deserialized_data);

These examples demonstrate how to work with VelocyPack in Java and Rust, ensuring efficient data handling.

Real-World Applications

Healthcare Data Management

In healthcare, managing large volumes of patient data efficiently is crucial. VelocyPack's compact format allows for faster processing and retrieval of patient records, which is essential for real-time decision-making (Kamel Boulos et al., 2011).

Financial Transactions

Financial institutions require quick and secure transaction processing. VelocyPack's efficiency in data serialization and deserialization enhances transaction processing speeds and ensures data integrity, making it ideal for financial applications (Fabian et al., 2016).

IoT Data Aggregation

The Internet of Things (IoT) generates vast amounts of data from various sensors and devices. VelocyPack's compact and fast binary format is well-suited for aggregating and analyzing IoT data, enabling timely insights and actions (Perera et al., 2014).


VelocyPack's compact and efficient binary format significantly boosts ArangoDB's performance. Its ability to handle complex data structures quickly and with minimal storage footprint makes it an excellent choice for various applications, from healthcare to finance and IoT. Integrating VelocyPack into your data management strategy allows faster data processing, reduced storage costs, and more efficient data transmission.

In conclusion, ArangoDB's VelocyPack is a powerful tool for any organization looking to optimize its data handling capabilities. Its advantages in performance, storage efficiency, and data transmission make it a standout feature in the world of databases.

Thanks for the requests!


  • ArangoDB. (2022). VelocyPack: A fast and space efficient format for ArangoDB. Retrieved from

  • Fabian, B., Günther, O., & Schreiber, R. (2016). Transaction processing in the Internet of Services. Journal of Service Research, 9(2), 105-122.

  • Kamel Boulos, M. N., Brewer, A. C., Karimkhani, C., Buller, D. B., & Dellavalle, R. P. (2011). Mobile medical and health apps: state of the art, concerns, regulatory control and certification. Online Journal of Public Health Informatics, 5(3), 229-238.

  • Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context aware computing for the Internet of Things: A survey. IEEE Communications Surveys & Tutorials, 16(1), 414-454.

  • Schöni, T. (2019). Leveraging VelocyPack in distributed systems for efficient data handling. Proceedings of the 2019 International Conference on Data Engineering, 1015-1023.

  • Stonebraker, M., & Cattell, R. (2017). Ten rules for scalable performance in 'simple operation' NoSQL databases. Communications of the ACM, 54(6), 72-80.