Data management done right.
Written by Mikhail Komarov on 01 Dec 2021
This post is about =nil; Database Management System called =nil; ‘DROP DATABASE * - the second part of =nil; Foundation mentioned within our Twitter “About” section (“Home foundation for =nil; Crypto3 and =nil; DBMS projects”).
Warning: I don’t really like to use marketing-purposed names, but this post is an explanatory one, so I have no other choice.
Providing a single cluster’s compressed data (a proof of any kind) to another one introduces a perfect and secure way to append the data to the second cluster, assuming some other data is absolutely and definitely present in the first cluster. Sounds complicated, but this is what literally “zk-bridging” is about.
But, when it comes to applications, more explicit data from the first cluster is required to be provided (e.g. asset amount which should be issued on the second cluster’s - Ethereum - side) for the application to be fully functional. This is required because first cluster’s (e.g. Mina, Solana, others) state proof deployed to the second cluster (e.g. Ethereum, Solana, others) only allows to check the explicitly provided afterwards data is correct and IS or WAS present in the source cluster. And such data retrieval still requires for the source cluster node to be up and running. Even some remote and untrusted one, but it has to be present.
Regular solution to that is to get all the necessary nodes up and running. But wouldn’t it mean large scale deployment, API provisioning and maintenance expenses for thousands of clusters? Yes. That would be a disaster in terms of maintenance. This also introduces a need to develop a unique API to each cluster (database) to be able to provide a consistent data allowing to generate state proof which would mean something.
For example, to prove Solana’s state it is required to retrieve the “Light-Client’s” state and append it’s proof with transactions proofs (or raw data) in case they are about to be “bridged”. Mina does require a different kind of state proof to be retrieved, but any particular application would require for the additional data to be retrieved as well (e.g. “bridged” asset amount).
Such data retrieval issues also often lead to the feeling that the data is hidden somewhere inside the node implementation instance and it is extremely hard to retrieve it. And when one finally retrieves it, it cannot be trusted.
Now, try to answer me. When was the last time when you hadn’t had such a feeling?
Maybe it was when you were browsing you files with your file manager?
Or, even better, maybe it was when you were writing some
SELECT FROM or
INSERT INTO SQL-query?
Don’t even try to argue. It was exactly back then.
This means the most comfortable way to access the reasonably-sized chunk of structured data is a query language. This usually gets achieved by indexing solutions which in most cases turn out to be a node deploy along with some DBMS (PostgreSQL, MySQL/MariaDB, others) deployed nearby, having a synchronizing service instance. This leads to at least three services deployed, a person dedicated to maintain them in case they are being used in production and at least several seconds delay (sometimes even more) between the data coming into the node and the data becoming available in the DBMS running nearby.
The query-language read-only (i.e.
SELECT FROM) access scalability
is achieved by hosting several nodes.
But, how to cheapen such a deployment cost back to what it was before? Is it
possible to cheapen it even more by reducing the amount of deployments for large
bridge proof processors (or any other folks keeping a lot of different protocol
nodes up and running)? And what about
To answer these questions we need to change the approach applied to the data management from what it currently is, to what it is within more mature, educated, experienced and less childish industries. The proposal is to take a look at Database Management Systems industry. Yes the one, where Amazon DynamoDB and Google BigTable along with PostgreSQL, MySQL/MariaDB or Apache Cassandra are already in play. The one, where people know something about data management.
Well, first of all, there would be no such amount of newborn bullshit terminology.
No “Blocks”. Fault-tolerant replicating databases work perfectly with per-transactional replication.
I have to admit in here that per-transactional replication makes it hard to maintain a proper cluster consistency with REALLY distributed cluster nodes. But anyway, time goes by, network becomes more boardwalk along with better connectivity, than it was back in 2008.
No “Chains”. Calling a cluster commit log which is simply handled by a little more complicated data structure, than a regular double-linked list (it is in most cases a single-linked list with identifiers being built as Merkle tree hashes actually) a “Chain” is ridiculous.
And, again, I have to admit that cluster commit log data structure differ from replication protocol to replication protocol.
Lots of other terms and notions would’ve never existed or would make much more sense.
Really? From the perspective of a DBMS industry it looks exactly vice versa. Actually, lots of cryptocurrency industry problems would’ve simply never existed if the proper approach was taken from the very beginning. Let us consider some of them.
The absence of each cluster’s access to each other’s cluster data brought to life a set of unnecessarily overcomplicated protocols, aiming to provide clusters with each other’s data read (the Graph, Celestia, etc) or write access (Polkadot, Cosmos, Wormhole, etc).
Database management systems, which are capable of providing read-write inter-database queries, don’t have such a problem by design.
Bitcoin, Ethereum, Solana, Avalanche and other replication protocols aiming to provide services either to the large amount of people either to high-load services are struggling with their state size. For some reason struggling replication protocols are trying to solve this issue by introducing a protocol-level solution, while more traditional and established way of solving this issue from the DBMS industry perspective is a state clustering. Sharding the oversized state via Paxos or Raft network-based consensus algorithms to several data-storing DBMS slave-nodes.
Yes, this means there would be a need to introduce a synchronization mechanism which would allow the sub-clustering. But it is still not a protocol-level solution, but just a software architecture.
The struggle, with which this whole conversation was started, is already solved within the DBMS industry for almost 30 years. Structured Query Language-alike dialects (for RDBMS) and more custom query languages (for so-called “NoSQL” databases) provide extensive access to the reasonably-sized data chunks (in case DBMS node operator doesn’t get nuts and starts to store large byte blobs inside) with a consistency strong enough for OLAP.
Node deployment and maintenance cost is another struggle this conversation was started from. Currently existing approach, which supposes for every replication protocol to have its own unique implementation reminds me of a situation within the DBMS industry in late 70s. Each protocol node instance, not designed to be run on the same machine (no matter, virtual or physical) with another protocol forces whoever wants to run them all to deploy separate pieces of hardware for them.
Perfect example is an old Graphene framework, used by Dan Larimer for his Steemit/EOS ventures. It was designed to use OS shared memory as an in-RAM storage, which made it literally impossible to coordinate a couple of instances running within the same OS instance because several node instances were interfering with each other’s shared memory allocations, causing failures.
Since, it is impossible to control the tech stack which every replication protocol uses for its implementation, the only way to run several databases using different replication protocols (e.g. Bitcoin and Ethereum and Solana) within the same hardware instance is run them with a DBMS.
So, having a single DBMS instance running several databases with replication protocols specific to each of them makes node maintenance cost cheaper in case the amount of hardware necessary to sub-shard the state of each of them is lower than the amount of hardware necessary to run each node independently.
Sure. =nil; ‘DROP DATABASE * project of ours (https://dbms.nil.foundation) is a database management system capable of handling fault-tolerant replication-enabled clusters. And when I’m talking about such clusters, I’m talking about existing protocols (e.g. Bitcoin, or Eth, etc) as well.
By using an old-good DBMS industry-specific way. Implementing a replication protocol adapter. Just like they do with MySQL/MariaDB replication protocol, for example: https://github.com/Begun/libslave.
Same approach works within =nil; ‘DROP DATABASE * as well. By implementing replication protocol adapter of a certain protocol family (Bitcoin, Litecoin, Bitcoin Cash, Feathercoin and others, for example, are of the same family), =nil; ‘DROP DATABASE * becomes the full-featured node of each of those clusters. And by running several databases with replication protocols, specific to them within a single DBMS node (e.g. Bitcoin and Ethereum and Polkadot), a DBMS instance becomes the full-featured node of each of them using the only piece of hardware (it should be a pretty performant one, but still a less performant which would’ve been required for three independent nodes) along with providing each of these databases with the same query language, state sharding and data access capabilities.
Funny thing, that some particular replication protocols (like Eth2) being considered from the DBMS point of view, will become several databases within the DBMS. Ethereum 2.0’s shards, for example, should be considered as separate databases. One shard - one database. So =nil; DBMS is not only about managing the data of different databases (of different clusters), but about managing the data of different clusters (shards) of a single database as well.
Replication protocol is not a term I’ve invented just to name some already known mechanism differently just to make it look like it is something new. “Replication protocol” is a very old and widely known term among database management system industry meaning the same as crypto wheel-reinventing industry calls a “Gossip protocol”.
So, the NUANCE. Replication protocol implementation has very little to do with “virtual machine” compatibility. Single replication protocol (some particular one, lets say, Bitcoin) implementation mostly covers all the other same-family protocols, compatible on a “Gossip protocol” level (e.g. Bitcoin Cash, Litecoin, others).
Another example is libp2p compatibility automatically brings the basis to enable the replication of any other database using such a toolchain.
State Size: Sub-clusterization. Fault-tolerant full-replica cluster with commit log built with authenticated data structure every member of which handles its state sharded with Raft is something =nil; ‘DROP DATABASE * does by default.
Data Accessibility: That is right, accessibility, not the “availability” thing. Availability is a read-only property. Accessibility supposes read-write properties.
Bitcoin and Ethereum families replication protocol adapters along with several own replication protocols implementations (DBMS-based fault-tolerant full-replica cluster, Raft) (with all of them available for sub-clusterization) are provided by default. More replication protocol adapters are coming.
Inter-database read/write queries are one more crucial component of data
accessibility. Providing a user with
SELECT FROM BTC.TABLE1 WHERE ... AND FROM
ETH.TABLE4 WHERE ... along with
INSERT INTO BCH.TABLE2 (SELECT FROM
SOL.TABLE1...-alike queries is a crucial component.
Maintenance Costs: Processing several databases with the same piece of software makes internal data management techniques consistent, so that allows to eliminate the reason why several protocol implementations usually do not get launched on top of the same hardware - inconsistent with each other implementations (obviously no protocol supposes for the other protocol’s daemon to be launched on the same hardware).
At the same time, swappable storage engines do not allow databases, clusters of which use replication protocols requiring constant and rapid state traversals (e.g. Ethereum-alike protocols), to degrade with its replication and data management performance with using a special-purposed data storage engines, specific for the particular replication protocol.
Simple. Replication protocol adapters require lots of very different cryptography to be used. Some of them use exotic hashes, some of them use zero-knowledge proof systems, others use exotic signatures. Handling them all using third-party modules results in the need for them to be patched and adjusted anyway. So developing a cryptography suite of our own turned out to be easier.
Bridges need data accessibility layer. Not the availability (it is a read-only thing), but accessibility (read-write thing). The connection between the =nil; ‘DROP DATABASE * and our bridge project will be outlined in a dedicated post. Stay tuned!
It will be published after moderation.
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