# Data Sketches: Efficient Algorithms for Big Data Analysis

In the era of big data, processing and analyzing massive datasets has become a significant challenge. Traditional algorithms often struggle with the sheer volume of information, leading to impractical computation times and excessive memory usage. Enter data sketches: a family of probabilistic algorithms designed to provide approximate answers to queries on large datasets with remarkable efficiency [1].

## Table of Contents

- What are Data Sketches?
- Key Advantages of Data Sketches
- Common Types of Data Sketches
- Comparison of Data Sketch Algorithms
- Implementing Data Sketches
- Conclusion
- References

## What are Data Sketches?

Data sketches are probabilistic data structures that can answer queries about large datasets using sub-linear space and time complexity. They trade a small amount of accuracy for substantial gains in speed and memory efficiency. These algorithms maintain a compact "sketch" of the data that can be used to answer specific types of queries [2].

## Key Advantages of Data Sketches

**Space Efficiency**: Data sketches typically use orders of magnitude less memory than the original dataset [3].**Time Efficiency**: Queries can be answered in constant or near-constant time, regardless of the dataset size [3].**Streaming Compatibility**: Many data sketch algorithms can process streaming data, updating their state on-the-fly [4].**Mergeable**: Sketches from different data sources can often be combined to represent the union of the datasets [5].**Deterministic Bounds**: Many sketches provide deterministic bounds on estimation error, ensuring reliable results [6].

## Common Types of Data Sketches

### 1. Theta Sketch

The Theta Sketch is used for estimating the number of unique items in a set or the intersection between sets [7].

**Logic**:

- Each item is hashed to a 64-bit value.
- The sketch maintains a threshold θ (theta) and keeps all hash values below this threshold.
- The estimate of unique items is: (number of stored values) / θ.
- Set operations are performed by applying set theory to the stored hash values.

**Use Case**: Estimating unique users across multiple datasets or calculating the Jaccard similarity between large sets.

### 2. HyperLogLog (HLL)

HyperLogLog estimates the number of distinct elements (cardinality) in a dataset using logarithmic space complexity [8].

**Logic**:

- Hash each item to a binary string.
- Use the first k bits to determine which of 2^k registers to update.
- The remaining bits are used to update the register with the position of the leftmost 1-bit.
- The estimate is derived from the harmonic mean of the register values.

**Use Case**: Counting unique visitors to a website or unique items in a large inventory.

### 3. Tuple Sketch

Tuple Sketches extend Theta Sketches by associating a summary value with each unique item [9].

**Logic**:

- Similar to Theta Sketch, but each stored hash is associated with a tuple of values.
- Updates to the sketch can modify these associated tuples.
- Allows for more complex aggregations beyond simple counting.

**Use Case**: Calculating metrics like average session length for unique users.

### 4. Quantiles Sketch

The Quantiles Sketch approximates the cumulative distribution function of a dataset, allowing for percentile and quantile estimation [10].

**Logic**:

- Maintains a compact representation of the data distribution.
- Uses a divide-and-conquer approach to merge and compress data.
- Can answer quantile queries (e.g., median, 95th percentile) with bounded error.

**Use Case**: Monitoring system latencies, analyzing user engagement metrics, or any scenario requiring order statistics on large datasets.

### 5. Frequent Items Sketch

This sketch identifies and estimates the frequency of the most common items in a stream of data [11].

**Logic**:

- Maintains counters for a subset of items seen in the stream.
- Uses probabilistic counting techniques to estimate frequencies.
- Can identify items that exceed a specified frequency threshold.

**Use Case**: Identifying popular items, detecting trending topics, or finding heavy hitters in network traffic.

## Comparison of Data Sketch Algorithms

To help you choose the right data sketch for your needs, here's a comparison table of the algorithms we've discussed:

Algorithm | Pros | Cons | Supported Operations |
---|---|---|---|

Theta Sketch |
• Excellent for set operations • Highly accurate for large cardinalities • Configurable trade-off between size and accuracy |
• Not suitable for frequency estimation • Slightly larger sketch size compared to HLL |
• Unique count estimation • Set intersection and union • Jaccard similarity |

HyperLogLog (HLL) |
• Very compact sketch size • Excellent for cardinality estimation • Good performance for stream processing |
• Not suitable for set operations • Less accurate for small cardinalities |
• Unique count estimation • Union of sketches |

Tuple Sketch |
• Combines set operations with tuple values • Flexible for custom analyses • Supports complex aggregations |
• Larger sketch size than Theta Sketch • More complex to implement and use |
• Set operations with associated values • Custom aggregations • Unique count estimation |

Quantiles Sketch |
• Estimates percentiles and quantiles • Works well on both integers and floating-point values • Supports merging of sketches |
• Less accurate for extreme percentiles • Larger sketch size for high accuracy |
• Percentile estimation • Quantile estimation • Histogram construction • Rank estimation |

Frequent Items Sketch |
• Identifies and estimates frequency of common items • Works well with skewed distributions • Supports deletes (with caveats) |
• May miss some frequent items • Not suitable for uniform distributions |
• Heavy hitters identification • Frequency estimation • Error bounds on estimates |

This table provides a high-level overview of each algorithm's strengths, limitations, and primary use cases. When choosing a data sketch algorithm, consider your specific requirements in terms of accuracy, memory usage, supported operations, and the nature of your data distribution [15].

## Implementing Data Sketches

While Apache DataSketches provides implementations in Java, C++, and Python [12], let's look at a production-ready implementation of HyperLogLog in Rust, inspired by the C++ implementation in ClickHouse [16]. This will give us insight into how these sketches are implemented in high-performance systems.

```
use std::collections::hash_map::DefaultHasher;
use std::hash::{Hash, Hasher};
// Constants for HyperLogLog configuration
const BUCKET_COUNT_LOG2: usize = 14;
const BUCKET_COUNT: usize = 1 << BUCKET_COUNT_LOG2;
const TIER_MASK: u64 = (1 << BUCKET_COUNT_LOG2) - 1;
const MAX_RANK: u8 = 63;
pub struct HyperLogLog {
// Store the maximum rank (number of leading zeros + 1) for each bucket
buckets: Vec<u8>,
}
impl HyperLogLog {
pub fn new() -> Self {
HyperLogLog {
// Initialize all buckets to 0
buckets: vec![0; BUCKET_COUNT],
}
}
pub fn add(&mut self, value: impl Hash) {
// Hash the input value
let mut hasher = DefaultHasher::new();
value.hash(&mut hasher);
let hash = hasher.finish();
// Use the lower BUCKET_COUNT_LOG2 bits to determine the bucket
let bucket = (hash & TIER_MASK) as usize;
// Count the number of leading zeros in the remaining bits, plus one
let rank = (hash >> BUCKET_COUNT_LOG2).trailing_zeros() + 1;
// Update the bucket if we've found a new maximum rank
if rank > self.buckets[bucket] as u32 {
self.buckets[bucket] = rank.min(MAX_RANK as u32) as u8;
}
}
pub fn merge(&mut self, other: &HyperLogLog) {
// Merge by taking the maximum rank for each bucket
for (a, b) in self.buckets.iter_mut().zip(other.buckets.iter()) {
*a = (*a).max(*b);
}
}
pub fn estimate(&self) -> f64 {
let mut sum: f64 = 0.0;
let mut zeros: usize = 0;
// Calculate the harmonic mean and count zeros
for &value in &self.buckets {
sum += 1.0 / (1u64 << value) as f64;
if value == 0 {
zeros += 1;
}
}
// Raw estimate
let e = BUCKET_COUNT as f64 * BUCKET_COUNT as f64 / sum;
// Apply correction factor
let h = e * self.alpha();
// Linear counting for low cardinalities
if h <= 5.0 * BUCKET_COUNT as f64 {
if zeros > 0 {
return BUCKET_COUNT as f64 * (BUCKET_COUNT as f64 / zeros as f64).ln();
}
}
// Apply correction for large cardinalities
if h <= 1.0 / 30.0 * (1u64 << 63) as f64 {
h
} else {
-1.0 * (1u64 << 63) as f64 * (1.0 - h / (1u64 << 63) as f64).ln()
}
}
fn alpha(&self) -> f64 {
// Correction factor based on the number of buckets
match BUCKET_COUNT {
16 => 0.673,
32 => 0.697,
64 => 0.709,
_ => 0.7213 / (1.0 + 1.079 / BUCKET_COUNT as f64),
}
}
}
fn main() {
let mut hll = HyperLogLog::new();
// Add some items
hll.add("apple");
hll.add("banana");
hll.add("cherry");
hll.add("date");
hll.add("elderberry");
println!("Estimated cardinality: {:.2}", hll.estimate());
// Create another HLL and merge
let mut hll2 = HyperLogLog::new();
hll2.add("fig");
hll2.add("grape");
hll.merge(&hll2);
println!("Estimated cardinality after merge: {:.2}", hll.estimate());
}
```

This implementation includes several key features of a production-ready HyperLogLog:

**Fixed-size buckets**: We use 2^14 buckets, which provides a good balance between accuracy and memory usage for most applications.**Efficient hashing**: We use a 64-bit hash and split it into bucket index and rank, minimizing the number of hash function calls.**Merge operation**: The`merge`

method allows combining two HyperLogLog sketches, crucial for distributed computing scenarios.**Sophisticated estimation**: The`estimate`

method uses different estimation techniques based on the observed values:- Linear counting for low cardinalities
- HyperLogLog estimation with bias correction for medium to high cardinalities
- Large range correction for very high cardinalities
**Bounded ranks**: We limit the maximum rank to 63, preventing overflows and improving accuracy for very large cardinalities.

This implementation demonstrates how data sketches can be implemented efficiently in a systems programming language like Rust, enabling high-performance cardinality estimation in large-scale data processing systems.

## Conclusion

Data sketches algorithms offer a powerful approach to processing and analyzing big data efficiently. By providing approximate answers with theoretical error bounds, they enable real-time analytics and insights on massive datasets that would be impractical to process using exact methods. As data continues to grow in volume and velocity, the importance of these probabilistic techniques in data science and engineering will only increase [13].

The Apache DataSketches library provides a robust set of sketch implementations with strong theoretical guarantees, making it an excellent choice for organizations dealing with big data challenges. Remember, while data sketches provide tremendous benefits in terms of efficiency, it's crucial to understand their probabilistic nature and the trade-offs between accuracy and resource usage when applying them to your specific use case [14].

## References

[1] Cormode, G., Garofalakis, M., Haas, P. J., & Jermaine, C. (2012). Synopses for massive data: Samples, histograms, wavelets, sketches. Foundations and Trends in Databases, 4(1–3), 1-294.

[2] Cormode, G. (2011). Sketch techniques for approximate query processing. Foundations and Trends in Databases, Now Publishers Inc.

[3] Apache DataSketches. (n.d.). Key Features. Retrieved from https://datasketches.apache.org/docs/KeyFeatures.html

[4] Muthukrishnan, S. (2005). Data streams: Algorithms and applications. Foundations and Trends in Theoretical Computer Science, 1(2), 117-236.

[5] Apache DataSketches. (n.d.). Theta Sketch Framework. Retrieved from https://datasketches.apache.org/docs/Theta/ThetaSketchFramework.html

[6] Apache DataSketches. (n.d.). Accuracy. Retrieved from https://datasketches.apache.org/docs/Theta/ThetaAccuracy.html

[7] Apache DataSketches. (n.d.). Theta Sketch. Retrieved from https://datasketches.apache.org/docs/Theta/ThetaSketchFramework.html

[8] Apache DataSketches. (n.d.). HLL Sketch. Retrieved from https://datasketches.apache.org/docs/HLL/HLL.html

[9] Apache DataSketches. (n.d.). Tuple Sketch. Retrieved from https://datasketches.apache.org/docs/Tuple/TupleOverview.html

[10] Apache DataSketches. (n.d.). Quantiles Sketch. Retrieved from https://datasketches.apache.org/docs/Quantiles/QuantilesOverview.html

[11] Apache DataSketches. (n.d.). Frequent Items Sketch. Retrieved from https://datasketches.apache.org/docs/Frequency/FrequentItemsOverview.html

[12] Apache DataSketches. (n.d.). Overview. Retrieved from https://datasketches.apache.org/docs/TheChallenge.html

[13] Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.

[14] Leskovec, J., Rajaraman, A., & Ullman, J. D. (2020). Mining of massive datasets. Cambridge university press.

[15] Cormode, G., & Hadjieleftheriou, M. (2010). Methods for finding frequent items in data streams. The VLDB Journal, 19(1), 3-20.

[16] ClickHouse. (n.d.). HyperLogLog implementation. Retrieved from https://github.com/ClickHouse/ClickHouse/blob/master/src/AggregateFunctions/AggregateFunctionUniq.h