#bloom filter

I would like to reflect on what I built last year through the lense of a software architect. This is the first installement in a series of architecture articles.

Last year, I had the opportunity to design and implement an archiving service to address GDPR requirements. In this article, I’ll share how we tackled this challenge, focusing on the architectural picture.

Introduction

This use case revolves around the Log Data Shipper, a core system component responsible for aggregating logs from multiple applications. These logs are stored in Elasticsearch, where they can be accessed and analyzed through an Angular web application. Users of the application (DPOs) can search the logs according to certain criteria, here is an example of such a search form:

The system then retrieves logs from ES so that the user can sift through them or export them if necessary, basically do what DPOs do.

As the volume of data grows over time, storing it all in Elasticsearch becomes increasingly expensive. To manage these costs, we needed a solution to archive logs older than two years without compromising the ability to retrieve necessary information later. This is where our archiving service comes into play.

Archiving Solution

Our approach involved creating buckets of data for logs older than two years and storing them on an S3 instance. Since the cost of storing compressed data is very low and the retention is very high, this was a cost-effective choice. To make retrieval efficient, we incorporated bloom filters into the design.

Bloom Filters

A bloom filter is a space-efficient probabilistic data structure used to test whether an element is part of a set. While it can produce false positives, it’s guaranteed not to produce false negatives, which made it an ideal choice for our needs.

During the archiving process, the ETL processes batches of logs and extracts essential data. For each type of data, we calculate a corresponding bloom filter - each search criteria in the form -. Both the archives and their associated bloom filters (a metadata field) are stored in S3.

When a user needs to retrieve data, the system tests each bloom filter against the search criteria. If a match is found, the corresponding archive is downloaded from S3. Although bloom filters might indicate the presence of data that isn’t actually in the archive (a false positive), this trade-off was acceptable for our use case as we are re-loading the logs in an temporary ES index. Finally we sort out all unecessary logs and the web application can read the logs in the same way current logs are consulted. We call this process "hydration".

Conclusion

This project was particularly interesting because it tackled an important topic – GDPR compliance – while allowing us to think outside the box by applying lesser-known data structures like bloom filters. Opportunities to solve challenging problems like this don’t come often, making the experience all the more rewarding.

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Czym jest Filtr Blooma

Burton Howard Bloom w 1970 roku opracował filtr (nazywany teraz Filtrem Blooma), który służy do testowania czy dany element znajduje się w zbiorze, czy nie. Filtr nie jest doskonały, gdyż możliwa jest fałszywa odpowiedź pozytywna ale już negatywna nie. Czyli odpowiada na pytanie "Czy dany element znajduje się w zbiorze?" odpowiedziami "nie" lub "może". Mimo wszystko w wielu przypadkach może być to ogromna oszczędność mocy obliczeniowej oraz pamięci.

original source: here.

Introduction

There is a huge number of reasons behind the success of Bitcoin. Let’s call several of them:

The system is decentralized, which means that nobody can control its processes alone. It is impossible to turn off the whole system upon someone's desire. All nodes have equal rights, and the only way to attack this network is to acquire the majority of performance.

It requires no registration. You don’t have to disclose your personal data to be able to join other network participants. In contradiction to traditional banking systems that officially request your data and may use it thereafter, Bitcoin interacts with you just like with other strangers.

It is easy to join. All you need is software to operate with blockchain data and exchange it with other network participants. Even inexperienced users can join Bitcoin.

It is heavily protected. Bitcoin uses different cryptographic approaches to support assets safety. Hacking or breaking the system using brute force is close to impossible.

There are many other reasons why Bitcoin is so popular nowadays, yet it has drawbacks. Data protection gives no chance for asset recovery in case of password loss. Decentralization does not allow involving any third party to resolve property conflicts. Every strong suit of the system is at the same time its weakness. One of those weaknesses is low privacy as a result of a chosen transactions system.

Transactions And Privacy

A transaction is the only way of guaranteed assets transfer between two participants in Bitcoin. In theory, one can try to mine a new block and send the block reward to another. But this approach is absolutely unreliable and cannot be counted on.

Each successful assets transfer consists of two parts. To be valid, they have to fit each other. Otherwise, the assets may be lost forever. In order to unlock money sent to a particular address, one needs to use a public key. This key should be used every time new assets arrive to the same account. Since all data is available for every system member, it is possible to trace transferred funds, which is a clear gap in privacy.

There is a way to hide assets though. Using a new private key every time allows you to segregate transactions as new public keys will be used. But this solution is available for full nodes only since light nodes commonly query for transactions. It discloses which transaction belongs to a particular node, especially taking into account that a standard network data exchange uses a unique network address.

Lightweight Node Privacy

Light nodes still have an option to protect their privacy. It is possible due to a special technique called the Bloom filter. It was implemented in the form it was described in a correspondent BIP. The key idea of the Bloom filter is that a light node can ask for many different transactions data. As soon as the data is received, unnecessary transactions get dropped. The main problem here is the balance between a transferred data amount and privacy assurance.

Earlier, we already talked about the block content. In order to request for transaction data, a light node should rather ask for its hash. The hash is taken from the Merkle tree that is queried partially. After the Merkle tree part is received, the node can check desired transactions and manage correspondent assets.

The Bloom filter implementation is optional though. As it is explained in the BIP, it is up to the user to decide whether to use it or not. If not, the user should be aware of security issues. One of disadvantages of the Bloom filter is the possibility of a false-positive result. This means that once the filter is received, it may show that a transaction exists while it actually doesn’t.

Afterword

Unfortunately, the core algorithm of the Bloom filter is too big to be explained in a single article. According to the specification, it uses three different commands, a set of flags, and various data types. All these topics will be discussed in the following articles.

Also for reading:

https://blockspoint.com/articles/market/4-best-platforms-for-blockchain-dapp

Bloomd

Bloomd is a high-performance C server which is used to expose bloom filters and operations over them to networked clients. It uses a simple ASCI protocol which is human readable, and similar to memcached.

Features

Scalable non-blocking core allows for many connected clients and concurrent operations

Implements scalable bloom filters, allowing dynamic filter sizes

Supports asynchronous flushes to disk for persistence

Supports non-disk backed bloom filters for high I/O

Automatically faults cold filters out of memory to save resources

Dead simple to start and administer

FAST, FAST, FAST

Whats is a Bloom Fliter?

A Bloom filter is a data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set.

A Bloom filter consists of two components: a set of k hash functions and a bit vector of a given length. We choose the length of the bit vector and the number of hash functions depending on how many keys we want to add to the set and how high an error rate we are willing to put up with -- more on that a little bit further on.

All of the hash functions in a Bloom filter are configured so that their range matches the length of the bit vector. For example, if a vector is 200 bits long, the hash functions return a value between 1 and 200. It's important to use high-quality hash functions in the filter to guarantee that output is equally distributed over all possible values -- "hot spots" in a hash function would increase our false-positive rate.