Author Archives: ksbhaskar

… But YottaDB Does the Hard Work for You

Part 1 discussed what ACID transactions are and why they are hard to scale. This post discusses how YottaDB does the hard work for you.

Concurrency Control

At a very high level, there are two types of concurrency control: pessimistic (known as locking) and optimistic.

In pessimistic concurrency control, as the transaction logic executes, it “locks” the data it accesses. To ensure fully ACID properties, this lock must prevent concurrent processes executing their transaction logic from both reading as well as writing the data. Reading is blocked so that the other processes do not see the intermediate states of data. The consequent performance loss has led databases to provide transaction variants that are not fully ACID, such as multiversion concurrency control (MVCC; also known as “stable reads”) where data a process has read within a transaction will not change for that process as a result of a commit by another transaction. While this suffices for some types of business logic (in the balance transfer example, if there is a concurrent change to a service charge, it is probably acceptable for the transaction to use the prior service charge), it is unacceptable for others (such as two concurrent transactions withdrawing funds from the same account).

In optimistic concurrency control (OCC), a process executing a transaction keeps track of the data that it reads, but does not update the database until the time comes to commit the transaction. If no data that it has read has changed (data that it intends to update must first be read), it commits the transaction; if any data has changed (“collided”), it starts over from the beginning and re-attempts the transaction. Optimistic concurrency control when implemented in hardware, is called transactional memory. Software transactional memory also exists.

The primary pitfall of pessimistic concurrency control is deadlock: if process 1 locks data A and process 2 locks data B, then as they execute program logic process 1 finds it needs B and process 2 finds that it needs A. Deadlocks can of course be more complex: for example, A needs a resource that B has locked, B has a resource that C has locked, and C has a resource that A has locked. In a complex environment where there might be thousands or tens of thousands of concurrent transactions, there might be a deadlock involving a few transactions. The system is busy, and the application is making progress overall, but those few transactions are mutually unable to progress further. Much research has gone into detecting and preventing deadlock, but the problem remains a hard one to solve at scale. We know of at least one database that provides Atomicity and Durability, but makes application software responsible for Consistency and Isolation.

The primary pitfall of optimistic concurrency control is livelock: if two processes update the same data, one of them will update it first. The other will detect a collision and retry the transaction, thus executing the transaction logic twice. At scale, the system is busy, but wasting resources because processes are executing transaction logic multiple times for each successful commit; at least they are not deadlocked.

While most databases use pessimistic concurrency control and ameliorate conflicts with MVCC, MVCC is unacceptable for real-time core-banking systems. YottaDB uses OCC with mechanisms to detect and limit livelock.

YottaDB Concurrency Control

Every database region has a transaction number that is incremented on every update.¹ Every database block that is updated during a transaction has its transaction number set to the transaction number of the region, which is incremented in anticipation of the next transaction.

A process inside a transaction notes the transaction number of every database block it reads (a block must be read before it can be updated). Updates are kept in process-private memory. When the process commits the transaction, it checks whether one or more blocks it has read were updated since it read them, and if no block has changed, it commits the transaction. If even one block has changed, it restarts the transaction.

To prevent persistent livelock, if a transaction is forced to restart the transaction thrice, on the fourth attempt it locks other processes from updating the database region(s) in the transaction, and completes the transaction.

As the probability of multiple concurrent transactions updating the same blocks is low, this works well in practice. Nevertheless, YottaDB has mechanisms for applications to detect and avoid livelock. Each database region has a count of the number of first, second, third, and subsequent² transaction restarts. In the typical scenario of random collisions, one would see a low rate of first restarts, a lower rate of second restarts, and virtually no third restarts. A pattern other than this means that there are pathological restarts resulting from application design that creates “hot” data areas where concurrent transactions update the same data. YottaDB has tools to enable application developers to identify such hot data areas, so that they can avoid pathological restarts.

To avoid a locking problems such as that described in Vendor’s secret ‘fix’ made critical app unusable during business hours, YottaDB also provides for transaction timeouts: a transaction that exceeds the specified time will abort the transaction with an error. Since the transaction will not have been committed, no other concurrent transaction is affected.

YottaDB’s OCC has additional benefits over locking:

• It is computationally easier to restart a transaction that has made no updates than to roll back a transaction that has.
• It is hard for pessimistic concurrency control to lock data that does not exist. With YottaDB’s OCC index blocks are part of the read set of a process executing a transaction. If another process adds a record when a process is inside a transaction, an index block will have a new transaction number, resulting in a restart. In other words, YottaDB transactions ensure Consistency and Isolation not just for the existence of data but also for the absence of data.
• Programming ACID transactions in YottaDB is extremely simple. For example:
  • There is no need to WATCH for changed data. YottaDB does the watching automatically with no need for application program action.
  • YottaDB automatically rolls back and restarts transactions, carrying them through to being committed, with no explicit programming required on the part of the application.

Yes, but does it really scale?

The proof of the pudding, as they say, is in the eating. YottaDB, and its upstream code base, GT.M (whose transaction processing implementation YottaDB inherits and improves), are live, in daily production use, at the world’s largest real-time core-banking systems, with tens of millions of accounts, and peak ACID transaction rates of tens of thousands of transactions per second.

YottaDB does the hard work for you by providing a clean, simple ACID transaction APIs in a variety of languages.

Contact us to discuss what YottaDB can do for your transaction processing application.

Footnotes

¹ YottaDB treats single updates as “mini transactions,” which simplifies both explaining and implementing how the database engine works.

² There are circumstances under which a transaction can restart more than three times. But that would be getting lost in the weeds for the level of detail of this blog post.

Credits

1. Photo of ATMs in Croatia by Gewild who dedicated this work to the public domain.
2. Photo of savings bank box of Second National Bank, Allentown PA is in the public domain.

… But YottaDB Does the Hard Work for You

What are ACID transactions?

ACID is an acronym for Atomic, Consistent, Isolated, and Durable.

Consider a transaction to transfer $100 from your savings account to your checking account. At a high level, it comprises at least the following steps:

1. If there is sufficient balance in the savings account:
1. Deduct $100 from your savings account balance.
2. Add $100 to your checking account balance.
2. Record information about the transaction, e.g., the origin of the request, authentication, whether it was authorized or denied (and if so, why), any service charges, etc.

Atomicity means that either all the steps need to be executed, or none of them need to be executed – you would be unhappy if the amount was deducted from your savings account but not deposited to your checking account, the bank would be unhappy if the amount was added to your checking account but not deducted from your savings account, the regulators would be unhappy if the transaction was not properly authorized and recorded, etc. If none of the steps happen, and you have enough balance in your savings account, you might be unhappy as a customer, but the bank and you are whole (“in balance”), and you can try again.

Durability means that once the transaction is completed, it is permanently recorded in the database and cannot be erased even if the computer crashes. If the bank later chooses to reverse the transaction, that would need to be a separate, second transaction. Durability will be the subject of a future blog post.

There is a duality to Consistency and Isolation. Consistency is the requirement that from a business perspective, the bank must be in compliance with business rules as determined by application logic. Consider two transactions executing at the same time (concurrently, but more about concurrency later). Both of them change the funds in accounts, and while they are executing, their temporary, private view is that the bank is not in balance (between steps 1a and 1b in the example above).

But when a process looks at any data other than what it is manipulating within the transaction, it should see the the bank as being in balance (Consistency). Isolation means that each transaction is executed as if it were the only transaction on the system. No application logic outside the transaction and executing concurrently with it should be able to see within the transaction, i.e., Isolation of a transaction provides Consistency for concurrent transactions.

So, for example, if there are two simultaneous requests to transfer funds from the savings account, and there are only enough funds for one, it is OK for one to be processed before the other, or the reverse, but each is processed as if it were the only transaction on the accounts. Of course, the second transaction would be rejected for lack of funds. Consistency and Isolation together imply serialization, that there is an order in which transactions are committed.

Why are ACID transactions hard at scale?

ACID transactions are conceptually simple. If an application processes only one transaction at a time, Consistency and Isolation are trivially implemented, while Atomicity and Durability only require a modicum of logic and programming effort. What makes them hard is scale. A large bank will at peak times process thousands to tens of thousands of transactions per second. Transaction processing at scale is challenging. In my thirty years in the database business, it has always been the case that as computers get faster, they are able to handle bigger workloads – but their ability to handle bigger workloads means that bigger workloads are thrown at them and push them to their limits. As the saying goes, “Every day, do more than what is expected of you, and soon more will be expected of you.”

The need to scale means that databases must execute in parallel the logic for multiple transactions, while ensuring ACID properties and serialization. The need to scale requires concurrency control. Concurrency control is the shepherding of the concurrently executing thousands to tens of thousands of transactions to get them committed with ACID properties. Concurrency control is just what make scaling ACID transactions hard.

Imagine you have a drive-through hamburger restaurant. You want to allow each hamburger to be customized, which means you can’t cook the patties in advance. Do you wait till an order is placed and then put patties on the grill, or when a car pulls into the parking lot, do you put a patty on the grill for each person in the car? If the former, then it takes more time to prepare each order. If the latter, you reduce the time to prepare each order, but you will have cooked patties leftover because not every person will order a hamburger.¹ Concurrency control to deliver Consistency and Isolation at scale involves similar trade-offs.

Continued in Part 2 …

Part 2 discusses methods for concurrency control, and how YottaDB’s implementation of concurrency control allows it to deliver ACID transactions at scale.

But you don’t need to wait for Part 2. You can Get Started with YottaDB right now!

Footnotes

¹ You could perhaps take each day’s leftover patties to make chilli con carne to serve the next day.

Credits

1. Photo of transaction in Hanoi by Radek Kucharski used under CC BY 4.0.
2. Image of Mandsur Opium Agency Hundi used under CC BY 4.0.

Core-banking systems (CBS) are the legal systems of record for account balances and transactions. For custodians with fiduciary responsibility for their customers’ money, CBS are the most mission-critical applications for commercial banks.

At a high level, there are two types of core-banking systems: batch and real-time.

Batch systems are process-centric, centered around an end-of-day (EOD) process that updates account balances from one day to the next.¹ Since transactions can arrive at any time, incoming transactions are placed in a memo file; credits and debits update account balances during EOD processing. This complicates processing logic, since if a debit transaction (e.g., an ATM withdrawal) is received during the day, the previous EOD balance and entries in the memo file must be reviewed to determine if there are sufficient funds in the account, and if there are, another memo must be posted to be processed at EOD. In batch systems, each transaction can be processed multiple times.

Real-time systems are customer-centric. A database has the current balance for each account. When a transaction arrives, it is processed forthwith, and the account balance updated. An incoming debit transaction only needs to be validated against the account balance – there is no memo file to review. Each transaction is processed just once and the account balance in the database is always current. Real-time systems have an additional advantage for financial institutions: since the account balance is always current, there is virtually no need to reconcile discrepancies.²

Batch systems originated on mainframes in the 1960s, with each part of the EOD processing involving the mounting of tapes and the movement of data from one tape to the next. Those days are of course long gone, and data now resides in databases, while batch processes move data within and between databases. Owing to their ability to process large numbers of accounts (accessing data sequentially is more IO efficient than random access), batch systems traditionally ran on expensive mainframes, and were used by the large financial institutions that could afford them. Since there is not necessarily a single point of truth for transactions and balances, banks that use batch systems must also retain staff to reconcile differences.

Real-time systems originated on minicomputers in the 1980s. Owing to their then limited ability to process data, real-time systems were more commonly used by smaller financial institutions. Apart from random access, real-time systems impose an additional constraint on databases: since a transaction is processed just once, straight through to a customer account, databases must support robust Atomic, Consistent, Isolated, and Durable (ACID) transactions and applications must use them correctly.³ Apart from using more affordable minicomputers, not needing staff to reconcile discrepancies was of course attractive to smaller institutions.

In the late 1990s and early 2000s, the massive scalability of the GT.M key-value database coupled with faster new-generation computers such as the DEC Alpha AXP OpenVMS, and UNIX servers from IBM, HP, and Sun, allowed the Profile real-time CBS to break out of its original savings-bank niche, and scale up to the needs of large financial institutions. The database’s ability to provide business continuity in the face of unplanned and planned events, and robust single points of truth for financial data on more affordable servers, delivered scalability with reduced capital, operating, and staff cost, facilitating its segue into global financial institutions.

Today, batch systems are legacy applications, and no financial institution would start implementing a new batch CBS. Yet legacy systems remain, because the cost to a bank of replacing a working CBS is large: replacing a CBS involves reorganizing business workflows to align them with those of the CBS. This also brings up a difference between banks in the United States and countries such as Thailand. Large banks in the US have grown by buying other banks. In such cases, it is often easier to retain the CBS of an acquired bank, implementing a front end application that provides customers with the illusion of a single bank, than to replace the acquired CBS. Large banks in countries like Thailand have grown organically, and thus process tens of millions of accounts on a single system vs. the millions of accounts that a large US bank may process on a single system.

YottaDB is a downstream derivative of GT.M. While staying upward compatible with GT.M, YottaDB adds language independence, better performance, and support for energy efficient ARM CPUs. Placid (Thailand) Ltd. took advantage of the benefits offered by YottaDB to build a greenfield real-time CBS (see Go+YottaDB – A Perfect Platform for Fintech).

Contact us to build your next mission-critical fintech application, or to migrate an existing application to YottaDB.

Footnotes

¹ Without getting too much into the weeds, note that the balance in an account may not be a single number: there can be an available balance, a collected balance, etc.

² Real-time systems also have EOD processes. For example, interest is usually computed on a daily basis. The important distinction is that financial transactions go straight through to customer accounts.

³ Vendor’s secret ‘fix’ made critical app unusable during business hours is an example of what can go wrong if an application uses the less than robust transactions implemented by a database. YottaDB’s transactions are more robust than those of that database.

Credits

1. Palazzo Salimbeni houses the main offices of Banca Monte dei Paschi di Siena. Established in 1472, it claims to be the world’s oldest surviving bank. Photo originally by Ray in Manilla used under CC by 2.0.
2. Picture of US Continental Congress eight dollar bill is in the public domain.

Given the range of hardware, operating systems, and file systems that we support, our internal network where we build and test YottaDB has a wide range of machines. The principal taxonomy is x86_64 vs. AARCH64 (ARM). In the former category, we have both custom-built PCs as well as off-the-shelf PCs and laptops; in the latter, our machines were all single-board computers (SBCs) until recently, as our view was that YottaDB on AARCH64 would primarily be used in embedded systems.

However, with the foothold of the ARM architecture in high performance computing – for example, the Fujitsu Fugaku supercomputer is seventh on the November 2025 Top 500 list – it seemed worthwhile to investigate AARCH64 systems other than SBCs. To that end, we purchased some refurbished 2023 Apple Mac Mini M2 pro systems and installed Ubuntu Asahi 24.04 LTS on them. Since we compile often during software development, the time to build YDB is a simple and quick way to compare systems, and was the basis for our initial comparison.

To say that we were blown away is putting it mildly. Our fastest machines now (A and B in the table below) are those Apple Mac Mini M2 pro systems. Our traditionally fastest x86_64 machine (D in the table) takes more than thrice as long as the faster Apple, and even the system we are setting up to release the forthcoming r2.04 on RHEL 10 (C in the table below) is almost twice as slow as the faster Mac Mini M2 pro. Our fastest SBC is E in the table below.

Here are the results.

 
In the above:

• Compile times are in seconds, as measured by time (make -j $(getconf _NPROCESSORS_ONLN) && make install) >compile.log. The reported number is the median of three runs. In practice, there was little variation between the runs.
• The number of CPUs was reported by getconf _NPROCESSORS_ONLN.
• All tests were run on an ext4 filesystem on an NVMe drive.

Based on these results, we will no longer consider embedded systems as the primary use case for YottaDB on AARCH64: ARM CPUs are clearly ready for high-end production applications.

Credits

1. Photo of Mac Mini M2 from an Apple Inc. web page.
2. Video of Ubuntu 24.04 LTS on Apple hardware from Asahi Ubuntu home page.

When Placid (Thailand) Ltd. started working on a greenfield FinTech application that would handle a mission-critical core-banking system at scale, they immediately chose YottaDB. Such a core-banking application requires both high performance as well as uncompromising robustness, and must deliver both at scale with large numbers of concurrent users.

Not all instances in the application use YottaDB, however — instances that handle data that’s used for reporting, for example, may not need to have the performance or the ability to handle concurrency that the core banking system does, and so they can use other databases.

When planning out the application architecture, the decision to use YottaDB as the data store was made even before deciding on the language to write the application in. The need for robustness and performance was the most important consideration, and combined with the fact that the YottaDB code-base has been production-tested for decades, it was the unquestioned choice. Ultimately, the team decided to write the application in Go, because it is a high-performance language and performance is the priority for this application.

Not every developer on the team interacts directly with YottaDB — and for those who don’t have experience with YottaDB’s native API, there would be a learning curve. Placid handles this by having a small, dedicated database team that creates a YottaDB framework that exposes a fintech-friendly API which allows other developers on the team to access YottaDB — they don’t need to know the YottaDB native API. This adds an element of future-proofing, by allowing the framework to be tweaked internally without requiring changes to the financial applications that use it.

As a result, the Placid team is able to combine the high performance, robustness, and scalability to large numbers of concurrent users with YottaDB, which matches the high performance that Go is known for. On an average day, the banking application handles one million customers.

Comsan Chanma, department manager of the SME team, said he’d recommend other teams follow a similar strategy when implementing YottaDB with the language of their choice — and mentioned that the choice to use YottaDB can and should be completely independent of the programming language. But by having a dedicated database team, you’re able to take advantage of YottaDB’s exceptional performance and consistency.

Credits

1. Photo of 1,000 baht bill appears to have no copyright.
2. Photo of plush Go gopher courtesy the Go Authors and released under the Creative Commons Attribution 3.0 Unported license.

TL;DR

Efficient access to critical sections is perhaps the single most important factor in determining database performance in high contention environments like large production systems. In our development of r2.04, we are paying a lot of attention to critical section access. This is a summary of our work and results to date (r2.04 is still under development as of this post’s publication date).

This post comes with the caveat that critical sections are only one determinant of application throughput. Other factors like compute time, or user input time may well be more important to your application. Even within an application, workloads vary: for example, the workload of interest posting in a bank is different from the workload of processing customer transactions.

What are Critical Sections?

Imagine a bus where just one person can board at a time. How fast one can load the bus depends on how long it takes each person to board the bus, and how long it takes between people boarding. Space taken by people waiting to board the bus can also be a consideration when space is limited, such as inside a bus terminal.

If there are only a few people waiting to board, no special organization is needed. They can cluster round the door, and as soon as one person boards, another can follow. But if there are more than a few, it will be more efficient overall if they queue, as shown in the picture above, rather than cluster around the door. If there are many more people wanting to board the bus, it would be best to have barriers to organize the queue. Barriers are especially important to keep queues organized if space is limited.

Contrast the case of people boarding a bus with patients consulting a doctor. Both are “one at a time” activities, in the sense that one person boards the bus at a time. and the doctor sees one patient at a time. But the strategies for organizing access are different. Since the time that it takes to board a bus is short, it makes sense for those waiting to board to stand outside the bus. Since the time a doctor spends with a patient is longer, one usually makes an appointment. In some intermediate scenarios like waiting to be served at a counter, one might take a ticket and wait for its number to be called. You don’t make an appointment to board a bus, and in most countries you don’t line up outside the doctor’s office.

A critical section in software is like the door of the bus. It is a section of code that only one process at a time can execute. Contention occurs when multiple processes all need to execute a critical section, like many people wanting to board the bus. In the course of r2.04, we invested heavily in analyzing and optimizing code, especially code that executes in critical sections. That is not discussed here; this blog post is about handling critical section contention.

Access to Critical Sections

Access to critical sections is conceptually like people boarding a bus.

If there are a small number of processes contending for access to a critical section, like a small number of people clustering around the bus door, it is probably most efficient for each process to just “spin” in a tight loop, and keep trying to get the critical section. This is efficient because there is no overhead, and as soon as the critical section becomes available, a waiting process will get it. The disadvantage of this approach is that a spinning process consumes a CPU. Since a computer has a limited number of CPUs, having more than a few spinning processes prevents processes that don’t need the critical section from doing useful work.

If there are many processes contending for access to a critical section, it makes sense for them to queue. As with people queuing for a bus, this can be a simple queue where the processes organize themselves into a queue, or a queue with barriers that requires the action of an external agent (e.g., someone to erect the barriers).

While this spin-and-queue approach is conceptually simple, the devil is in the details.

Implementing Critical Sections

GT.M / YottaDB

For many years, the upstream GT.M included its own code to control access to critical sections that YottaDB used unchanged. This implementation is as follows.

• Spinning is like people clustering around the bus door.
  • When a process is unable to get a critical section, it “hard spins” for a certain number of iterations, called the hard spin count, iterating in a tight loop to get the critical section. The r2.04 code base includes a PAUSE opcode to reduce the impact of its hard spin loops.
  • After the hard spin limit is reached, it “soft spins” for a certain number of iterations called the soft spin count. The difference between a hard spin and a soft spin is that in the latter the process executes a sched_yield() each time through the loop. This relinquishes the CPU, and moves the process to the back of the run queue. When it reaches the front and gets the CPU, it again tries to acquire the critical section. It tries soft spins for soft spin count iterations. An important difference between a hard spin and a soft spin is that the sched_yield() of each iteration of a soft spin causes a context switch. Context switches use operating system resources, and may involve critical sections within an operating system.
• Queuing is like people waiting in line. When a process does not get the critical section after its hard spins and soft spins it adds itself to a queue in shared memory. Adding itself to this queue itself requires a critical section, albeit a tiny one that executes almost instantly and does not require any special organization. When a process releases a critical section, it wakes up the first process in the queue, which then tries to get the critical section. If it does not get the critical section, it goes through the hard-spins and soft spins and queuing all over again.

Owing to the number of changes we made to the builtin implementation and its evident abandonment by GT.M, we refer to it as the “YottaDB mutex.”

Linux

Linux provides pthread_mutex functions that use a similar technique.

Fairness vs. Throughput

Neither mutex implementation is “fair.” A process that reaches the front of the queue could again be pushed to the back of the queue by another process that just started its attempt to acquire the critical section (called “barging,” it is not unlike a queue-jumper that walks up to the door of the bus and gets in, pretending not to see the queue).

Fairness isn’t free. Implementing a guaranteed first-in / first-out fairness would be computationally expensive to the point of being unacceptable for a high-throughput application. Practical code to handle contention balances fairness against throughput.

While spin-and-queue techniques work well across most workloads of typical applications, their unfairness can be exacerbated when a system is pushed to its limits, resulting in large numbers of processes contending for the same critical section. While some processes randomly execute faster, others equally randomly execute slower, i.e., under heavy loads, the unfairness can manifest itself as inconsistent response times and throughput, rather than a proportional slowdown of all processes.

From r1.24 (V6.3-006) to r1.26 (V6.3-007)

GT.M versions through V6.3-006 used the builtin implementation of critical sections, as did YottaDB releases through r1.24. GT.M versions starting with V6.3-007 use pthread_mutexes, as do YottaDB releases from r1.26 through r2.02. While we do not know the motivation for the GT.M change, the developers would have had a reason to make the change.

The r2.04 Critical Section Journey

While no customer or user of YottaDB had expressed any concerns about performance, and although each YottaDB release was slightly faster than the GT.M versions merged into its code base (owing to small performance enhancements we have made that the upstream developers have not adopted), we decided to make performance and scalability a key feature of r2.04.

The Journey Begins

The twin axioms of performance improvement are:

• Be careful what you measure, because that is what you will improve.
• If you choose well, you will improve performance overall.

We decided to start with an M program that computes lengths of 3n+1 sequences, since that is a database update intensive workload that can easily be scaled up and down in both problem size as well as the number of concurrent processes. Running the program with twice the number of concurrent processes as CPUs (i.e., with some contention, but not a heavy load), we observed the data in the table below.

• The first column is a GT.M version or YottaDB release. Each YottaDB release is preceded by a GT.M version, the latest one whose source code is merged into the YottaDB release. r2.03-pre is the YottaDB code under development for r2.04 with V7.1-002 code and our enhancements and fixes merged prior to the critical section contention work.
• The second column shows an average elapsed time (i.e., less is better) for benchmark runs.
• The third column compares the elapsed times with GT.M V6.3-006, the last GT.M version released before the switch to pthread_mutexes (i.e., larger – more positive – is better; smaller – more negative – is worse).
• The fourth column compares the performance of each YottaDB release with the GT.M version it includes (larger – more positive – is better; smaller – more negative – is worse).

 
The last row, labeled r2.03-pre+ is the build after we included a host of performance improvements to code that executes inside critical sections (analogous to the time taken by each person to board the bus), and before our changes discussed below. It shows that even prior to the enhancements discussed below, the evolving YottaDB r2.04 code executed the 3n+1 benchmark 24.8% faster than GT.M V7.1-002.

(We were somewhat surprised by the r2.02 number, because such a slowdown has been the experience of neither us nor our users, and no such slowdown was apparent in our testing prior to the release. While it is perhaps an artifact of a specific benchmark, it is nevertheless the number we saw, and as we are objective developers, it is the number we are reporting here.)

The “Culprit”

It didn’t take long to discover that the change from the builtin mutex to pthread_mutex from GT.M V6.3-006 to V6.3-007 was the cause of the apparent slowdown. When we reverted to the builtin mutex (whose code we carefully examined for speed-up opportunities, which we implemented), we found that performance had been restored!

Then began the quest to determine what benefit pthread_mutexes offered, that motivated the change in V6.3-007. So we expanded the simulated workloads. One was a workload that simulated interest posting by a bank during day-end processing, and the other was code that did nothing other than to stress critical section acquisition. We ran all three benchmarks up to 16,384 processes, which is an extreme level of stress. While a large application can have 16,384 processes, it would be unusual to have that many processes contending for the same critical section at the same time. In the bus boarding analogy, it would be like a large bus terminal with many buses and many people, but with everyone wanting to board the same bus at the same time.

Behavior under extreme stress of the interest posting benchmark is shown below. With the 3n+1 benchmark, the YottaDB mutex always performed better.

• Times are in milliseconds, i.e., smaller numbers are better.
• r2.02 is the current YottaDB production release.
• r2.03 corresponds to r2.03-pre+ in the table above, i.e., just prior to changes to use the YottaDB mutexes.
• r2.03+mutex is the code with the change to use YottaDB mutexes.

Two observations are evident from the graph.

1. The changes to YottaDB r2.03 show a clear improvement over r2.02, analogous to speeding up the time it takes for each passenger to board the bus.
2. YottaDB mutexes perform better than pthread_mutexes until around 2,000 processes, above which pthread_mutexes performed better. By analogy, when the number of people wanting to board the bus becomes large, externally organized queuing helps.

Adaptive Mutexes

That led us to the question as to whether it was possible to make mutexes adaptive, to use YottaDB mutexes at low to normal loads, to switch to pthread_mutexes under heavy loads, and to switch back under normal loads. By way of analogy, as the number of people wanting to board a bus increases to a point where they no longer organize themselves into a queue, one can bring in retractable belt barriers to organize a queue.

Important requirements of any adaptive method are:

• It must have minimal overhead: it should take minimal additional code to implement.
• Switching should be relatively inexpensive, especially the switch from YottaDB mutexes to pthread_mutexes. Since that switch happens under an increasing load, the switch should not further stress the system.
• It should have hysteresis, i.e., it should not bounce back-and-forth between the two techniques.
• It should not pretend to be universal: there will be workloads for which the adaptive method is not well suited.

Overhead is minimized by building on the existing database statistics, as reported by, for example, ZSHOW. While the statistics in the database fileheader record data from creation of a database file, switching is based on changes in the statistics.

A field in the database file header, which is mirrored in the shared memory segment for that region, records the current mutex type in use. Switching from YottaDB mutexes to pthread_mutexes is accomplished by setting the field to specify pthread_mutexes, and waking up all processes waiting for the mutex. There will be a momentary blip in CPU usage as the awoken processes execute pthread_mutexes. In an attempt to encourage fairness, the process initiating the switch awakens queued processes in the order in which they are queued; however, there is no guarantee that queue order will be preserved. Switching from pthread_mutexes to YottaDB mutexes is essentially the reverse, except that the processes are woken up by Linux, the manager of pthread_mutexes.

Switching from Linux mutexes to YottaDB mutexes occurs at a lower load than that at which switching from YottaDB mutexes to Linux mutexes occur. A heuristic that emulates a damped low-pass filter ensures that a load which randomly varies around the switching thresholds does not cause frequent switching back and forth.

The following graph shows the performance of the adaptive method. The interest posting benchmark is the same as the above graph; however, the results are from a different server. This benchmark showed similar results on all servers.

• Times are in milliseconds, i.e., smaller numbers are better.
• r2.02 is the current YottaDB production release.
• V7.1-002 is the GT.M version merged into the YottaDB code base.
• r2.03 (pthread mutex) corresponds to the r2.04 code base under development, with V7.1-002 merged,  prior to the change to use the YottaDB mutexes.
• r2.03+ (ydb_mutex) corresponds to the previous item, with the change to use YottaDB mutexes.
• r2.03+ (adaptive mutex) corresponds to the code with the adaptive method.

From the graph:

1. The r2.04 code base with V7.1-002 merged tracks V7.1-002 – any differences are just the randomness inherent to benchmarking.
2. Both the above show a significant improvement over 2.02.
3. Benchmarks that use the YottaDB mutex and the adaptive mutex track each other, and are significantly faster than the above at workloads through 2,048 processes.
4. Above 2,048 processes, the benchmark with the YottaDB mutex shows significantly worse performance compared to r2.03 (pthread mutex)  and V7.1-002, whereas the adaptive mutex tracks r2.03 (pthread mutex)  and V7.1-002.

In summary, the adaptive mutex adapts to the workload, and gives you the best option for the current load – you can have your cake and eat it too. In our analogy, it is as if when the number of people wanting to board the bus reaches a certain threshold, retractable belt barriers suddenly appear, and suddenly disappear when the number of people wanting to board drops.

Since there may be workloads for which the adaptive method does not work well, the YottaDB r2.04 code includes MUPIP SET commands to ensure that processes accessing a database region or file use only a specified mutex.

• mupip set -mutex_type=adaptive to specify Adaptive mutex, the default for newly created database files.
• mupip set -mutex_type=ydb to use the YottaDB mutex.
• mupip set -mutex_type=pthread to use pthread mutex.

You also have to use either the -region or the -file option to identify the regions or database files for which you wish to specify the mutex type.

What Now?

As of today, the mutex code for r2.04 is stable and merged into the master branch, ready for the r2.04 release. We are currently working on other code for r2.04. Although not suitable for production use, the master branch is stable for development, testing, and benchmarking. If you care to try the r2.04 master branch to see how mutexes perform with your application workloads, here’s how to install the master branch.

export tmpdir=<tmp directory> installdir=<ydb installation directory>
mkdir -p $tmpdir
wget https://download.yottadb.com/ydbinstall.sh
chmod +x ydbinstall.sh
sudo ./ydbinstall.sh --from-source --branch master --installdir $installdir --nolinkenv --nolinkexec --nopkg-config

Once you are done testing, simply remove the installed YottaDB from $installdir: sudo rm -rf $installdir.

If you do test it, please share your results. Thank you very much.

Credits

1. Photo of People Boarding a bus at Davenport and Oakwood, Toronto, in 1927 is in the public domain.
2. Photo of Mango Bus stand Jamshedpur bus terminal by Shahbaz26 released under the Creative Commons Attribution-Share Alike 4.0 International license.

We thank Lothar Jöckel for his first guest blog post, and hope there are many more to follow. If you would like to post on the YottaDB blog please contact us at info@yottadb.com.

TL;DR

If you’re a Nim developer interested in working with a powerful hierarchical NoSQL engine, or an M developer interested in working with powerful modern programming language then this is for you.

The combination of Nim’s modern language features with YottaDB’s battle-tested database engine creates a powerful stack for building high-performance, reliable systems. This binding bridges the gap between a database proven over decades of use in mission-critical applications and a contemporary systems programming language.

I’m pleased to announce nim-yottadb, a language binding that connects Nim with the YottaDB database. This gives you direct access to global and local variables, transactions, iteration, locks, and more – all from Nim.

A Simple Example

setvar:
    ^Users("john_doe", "profile", "name") = "John Doe"
    ^Users("john_doe", "profile", "email") = "john@example.com"

let userName = get: ^Users("john_doe", "profile", "name")
echo "Hello, ", userName

In this post, I want to walk you through:

• What YottaDB is (at a high level)
• Why binding it to Nim is interesting
• What features nim-yottadb currently offers
• How its API and DSL (Domain Specific Language) design works
• Caveats, threading, and implementation notes
• Examples and next steps

Key Features of YottaDB

YottaDB is a high-performance, schema-less, key-value database designed for extreme scalability and reliability, particularly in transaction-heavy environments. Its origin is the M (affectionately known as MUMPS) language and database, which has been battle-tested in mission-critical systems for decades.

Key-Value Data Model with a Hierarchical Twist

At its core, data is stored as sparse, multi-dimensional arrays. A “global” variable starts with a caret (^), can have subscripts, creating a natural tree structure. A global variable is just a key-value node that persists and is shared, i.e., it is in the database.

^Patients("Smith", "John", 2024, "Visit") = "Checkup"

This model is incredibly flexible (schema-less) and allows for efficient hierarchical data access.

Extreme Performance and Low Latency

YottaDB has a in-memory database engine with cooperating processes managing database files that use transaction journals for Durability. All data operations are performed directly in memory, making it exceptionally fast.

A daemonless database whose logic executes in the address spaces of processes accessing the database, with control structures in shared memory, eliminates connection overheads and minimizes resource contention. This yields massive scalability on multi-core servers.

Rock-Solid Reliability and ACID Transactions

YottaDB is proven in mission-critical applications where data loss is unacceptable (e.g., banks, hospitals). It provides fully ACID (Atomic, Consistent, Isolated, Durable), robust transaction processing system that uses write-ahead journaling. Database updates are first written to a journal file before being applied to the database, ensuring data can be recovered after a crash.

Tight Integration of Database and Programming Language

This is a hallmark of the M heritage. Database operations are simple commands within the M language. There is no separate query language (like SQL) or connection string. A simple command like SET ^Customer(123)="John" both updates the variable in memory and commits the change to the database. This eliminates object-relational mapping (ORM) overhead and makes the code very concise for data manipulation.

YottaDB brings this tight integration to languages other than M.

Mature and Robust Codebase

M technology has its roots in the 1960s MUMPS. YottaDB itself is a direct descendant of GT.M, which was first deployed in 1986. The code base has decades of use in mission-critical, high-availability systems, and is the database of record at some of world’s largest real-time core-banking systems.

Efficient Database Replication

Efficient database replication provides built-in, low-latency replication between database instances. This is crucial for creating hot-standby systems for disaster recovery. Replicas can also be used downstream from production systems for real-time analytics, reporting, etc.

Replication protects mission-critical applications like core banking systems, healthcare information systems, stock exchanges, and any other application that requires the ability to remain continuously available in the face of unplanned as well as planned events.

Nim

The Nim programming language is a statically typed, compiled systems language that has a unique and powerful set of features. It’s often described as having the performance of C or C++, the expressiveness of Python, and the safety of Rust or Ada.

Python-like Syntax with Static Typing

This is one of the most immediately appealing features. Nim’s syntax uses significant whitespace (indentation) like Python, making it clean and easy to read. Unlike Python, it’s statically typed, meaning type errors are caught at compile time, leading to more robust and performant code. The compiler does all the type checking before the program ever runs.

Looks like Python, but is statically typed and compiled!

proc greet(name: string, age: int): string =
    return "Hello, " & name & ". You are " & $age & " years old."

echo greet("Alice", 30)

Compiles to Efficient C, C++, and JavaScript

Nim doesn’t have a virtual machine. Instead, it compiles its source code down to another language. By compiling to C, C++, or even Objective-C, it achieves performance comparable to these native languages. The generated C code can be compiled on virtually any platform.

The JavaScript target allows you to write both your backend and frontend logic in the same language, enabling full-stack development with Nim.

Powerful Metaprogramming

This is one of Nim’s superpowers. You can generate code at compile time, reducing boilerplate and creating powerful Domain-Specific Languages (DSLs).

With Templates you perform simple syntactic substitutions (hygienic macros). They are like C macros but much safer and more integrated.

The most powerful feature is Macros. You can manipulate the Abstract Syntax Tree (AST) of your code at compile time. This allows you to implement new language features, validate complex conditions, or generate code based on custom logic.

Memory Safety and Control

Nim offers a pragmatic approach to memory management.

It comes with several built-in garbage collectors (e.g., deferred reference counting, mark-and-sweep, …). The default is fast and pause-free for most applications.

For systems programming or real-time applications where garbage collection pauses are unacceptable, you can use manual memory management (alloc(), dealloc()) or leverage the --gc:arc or --gc:orc (Owned Reference Counting) options, which provide deterministic, non-tracing memory management without a garbage collector.

Generics, Union Types, and More

Nim’s type system is both practical and expressive. Full support for generic programming, allows you to write flexible and reusable code for different types.

Sum Types (Variant Objects) let you define a type that can hold values of different, but fixed, types. This is excellent for modeling state.

With Distinct Types you create a new type that has the same underlying representation as an existing type but is considered incompatible with it (e.g., type Meter = distinct int and type Kilogram = distinct int prevents you from accidentally adding meters to kilograms).

Zero-Cost Abstraction and Efficiency

Nim is designed to be highly efficient.

• No Runtime Overhead: Features like iterators, templates, and generics are resolved at compile time, resulting in code that is as fast as hand-written C.
• Value Types: Structs are value types by default (stored on the stack), which is cache-friendly and fast.
• Direct Control: You have low-level control over memory layout, pointers, and can even inline assembly code when needed.

Unified Function Call Syntax (UFCS)

This syntactic sugar allows for both method-call and function-call syntax, where a.f(b) is equivalent to f(a, b). This enables a fluent, “chaining” style of programming that is very readable, similar to what you find in Unix pipes or Rust.

Cross-Platform and Interoperability

• Native Executables: Nim compiles to a single, dependency-free native executable, making deployment trivial.
• Excellent C/C++ Interoperability: You can directly import and call C/C++ functions and libraries with minimal effort, making it easy to leverage existing codebases.
• Cross-Compilation: It’s straightforward to compile for a different target platform (e.g., compile for Windows on a Linux machine).

Async/Await for Concurrency

Nim has a built-in async/await mechanism for writing highly scalable asynchronous I/O operations, similar to what you find in Python, JavaScript, or C#. This makes it well-suited for network servers and clients.

What nim-yottadb provides

Many environments use YottaDB already. With a Nim binding, you can write new components or tooling in Nim that integrate with existing YottaDB data.

The flexibility of Nim’s metaprogramming (macros, templates) enables a nicer API and DSL wrapper over the more “raw” C interface. You can mask lower-level details, make code more expressive, and reduce boilerplate.

Core (Simple-API)

The binding exposes a basic set of database operations, roughly mapping to YottaDB capabilities:

• ydb_data — inspect node or subtree state (e.g. whether there is data, subtree, both or neither)
• ydb_delete — delete a node or an entire subtree
• ydb_delete_excl — delete local variables except some exclusions
• ydb_get / ydb_set — read or assign the value of a local or global variable
• ydb_incr — atomic increment (local or global)
• ydb_lock — lock one or more global variables
• ydb_lock_incr / ydb_lock_dec — manipulate a lock count
• ydb_node_next / ydb_node_previous — traverse siblings or nodes in and out of order
• ydb_subscript_next / ydb_subscript_previous — step through subscript ranges under a global
• ydb_ci — call a routine (M / YottaDB “Call-In Interface”)

These correspond fairly directly to the YottaDB C API (or M primitives). The binding supports both single-threaded and multi-threaded modes (automatically selected when compiling with --threads).

Extensions & syntactic sugar

To make working with the binding more ergonomic, nim-yottadb adds:

Iterators

You can iterate over next/previous nodes or subscripts, looping over nodes instead of manually calling next node/sub.

YdbVar

YdbVar is a type with overloaded operators ($, []) so that a global can be referenced in a natural, array-like way.

DSL

Instead of using the simple API directly an alternative exists with a DSL (Domain Specific Language). The DSL offers Nim-style keywords/mnemonics for common operations. So instead of writing

ydb_setvar("^building", @["Room", "1", "size"], "22.5")

you can write

setvar: ^building("Room", 1, "size")=22.5

setvar / get

setvar:
    ^XX(1,2,3)=123
    ^XX("B",1)="AB"

Support for mixed type subscripts

setvar: ^X(id, 4711, "pi") = 3.1414

setvar: in a loop

for id in 0..<5:
    setvar:
        ^CUST(id, "Timestamp") = cpuTime()
        ^CUST(id, "loop") = id

increment

Increment a global in the database by 1

let nexttxid = increment: ^CNT("TXID")
let accid = increment: ^Customer(nexttxid, by=1000)

data

Test if a node or tree exists and has a subtree.

setvar:
    ^X(5)="F"
    ^X(5,1)="D"
dta = data: ^X(5)
assert YdbData(dta) == YDB_DATA_VALUE_DESC

delnode

Delete a node. If all nodes of a global are removed, the global itself is removed.

delnode: ^X(1) # delete node

deltree

Delete a subtree of a global. If all nodes are removed, the global itself is removed.

deltree: ^X(1)

lock

Lock upto 35 named lock resources. Other processes trying to lock a locked resource must wait until the lock is released. {} has to be used to lock multiple resources in one operation; an empty list releases all locked resources. If lock: is called again, the previous locks are automatically released first.

lock:
  {
    ^LL("HAUS", "11"),
    ^LL("HAUS", "12"),
    ^LL("HAUS", "XX"), # not yet existent, but ok
  }

Additional locks can be acquired or released, without affecting other locks held by a process, by prefixing lock resource names + or -.

The template withlock simplifies the locking further:

let amount = 1500.50
withlock(4711):
  setvar:
    ^custacct(4711, "amount") = amount
    ^booking(4711, "txnbr") = amount

On leaving the withlock block, the lock is automatically released.

nextnode / prevnode / nextsubscript / prevsubscript

Traverse a global/subscript in the collating sequence.

(rc, node) = nextnode: ^LL()
(rc, node) = prevnode: ^LL("HAUS", "ELEKTRIK", "DOSEN", "1")
(rc, subs) = nextsubscript: ^LL("HAUS", "ELEKTRIK")
(rc, subs) = prevsubscript: ^LL("HAUS", "FLAECHEN")

‘get’ with postfix

It is possible to enforce a type when getting data from YottaDB. By using a “postfix” an expected type can be defined and tested.

let i = get: ^global(1).int16
let f = get: ^global(4711).float32

If the value from the db is greater or smaller than the range defined through the postfix, a ValueError exception is raised.

The following postfixes are implemented:

• int, int8, int16, int32, int64
• uint, uint8, uint16, uint32, uint64
• float, float32, float64

The .binary Postfix

The binary postfix allows binary data of virtually unlimited size to be read from the DB. setcan save data, theoretically upto 99,999,999 MB.

let dbval = get: ^tmp(4711).binary

Saving a Nim Object-Tree to the database

Based on the Nim object model, it is possible to store objects, even complex ones, in the database. A global variable is created for each type, e.g., Address, Customer, etc. Attributes are then stored with their corresponding names.

type
  Address* = object of RootObj
    street*: string
    zip*: uint
    city*: string
    state*: string

let address = Address(street: "Bachstrasse 14", zip:6033, city:"Buchs", state:"AG")
store(@["4711"], address)

The data is stored as

^Address(4711,"city")="Buchs"
^Address(4711,"state")="AG"
^Address(4711,"street")="Bachstrasse 14"
^Address(4711,"zip")=6033

Performance

In general, the Nim / YottaDB language binding has excellent performance. Simple tests on a MacMini M4 with a virtualized Ubuntu (2 Cores / 4GB Memory) gives the following figures where every test had 10 million different records.

upcount dsl 2439 ms. (Increment a Global)
set dsl 2479 ms. (Set global value)
nextnode dsl 1536 ms. (Iterator over all nodes)
delnode dsl 2774 ms. (Delete all nodes)

This means writing 4,100,041 records per second and traversing the nodes at 6,510,416 nodes per second. I think these are impressive numbers!

A Larger Example

The following program retrieves images from a directory, stores them in the database, and extracts them into another directory.

import os
import std/[times, strutils]
import yottadb

proc walk(path: string): seq[string] =
    for kind, path in walkDir(path):
        case kind:
        of pcFile, pcLinkToFile:
            result.add(path)
        of pcDir, pcLinkToDir:
            result.add(walk(path))

proc loadImagesToDb(basedir: string) =
    for image in walk(basedir):
        let image_number = increment(^CNT("image_number"))
        setvar:
            ^images($image_number) = readFile(image)
            ^images($image_number, "path") = image
            ^images($image_number, "created") = now()

proc saveImage(target: string, path: string, img: string) =
    if not dirExists(target):
        createDir(target)

    let filename = path.split("/")[^1]
    let fullpath = target & "/" & filename
    writeFile(fullpath, img)

proc readImagesFromDb(target: string) =
    var (rc, subs) = nextsubscript: ^images(@[""]) # -> @["223"], @["224"], ...
    while rc == YDB_OK:
        let img     = getblob(^images(subs))
        let path    = get(^images(subs, "path"))
        saveImage(target, path, img)
        (rc, subs) = nextsubscript: ^images(subs)

if isMainModule:
    loadImagesToDb("./images") # read from the folder and save in db
    readImagesFromDb("./images_fromdb") # read from db and save under this folder

Conclusion

The nim-yottadb binding successfully bridges two powerful technologies from different eras of computing. YottaDB brings decades of refinement in hierarchical data management and transaction processing, while Nim offers modern language features, metaprogramming capabilities, and performance characteristics that rival lower-level systems languages.

What makes this integration particularly compelling is how Nim’s DSL capabilities and clean syntax make YottaDB’s hierarchical data model feel natural and expressive. The ability to write database operations that look like native Nim code, while maintaining the performance and reliability of a battle-tested database engine, represents the best of both worlds.

The performance benchmarks demonstrate that this binding doesn’t sacrifice speed for convenience—Nim applications can leverage YottaDB’s capabilities with minimal overhead, making it suitable for the same high-performance, transaction-heavy use cases that YottaDB has traditionally served.

For developers working with existing YottaDB systems, nim-yottadb provides a path to modernize tooling and develop new components without abandoning proven database infrastructure. For Nim developers, it opens access to a unique class of hierarchical database that excels in scenarios where relational databases might struggle.

As the binding continues to evolve, it represents not just a technical achievement, but a practical solution for building robust, high-performance systems that need both modern development ergonomics and proven data reliability. Whether you’re extending legacy M applications or building new systems from scratch, nim-yottadb offers a compelling combination of performance, reliability, and developer experience.

The project is available on GitHub as nim-yottadb, and welcomes contributions from both the Nim and YottaDB communities.

About Lothar Jöckel

Lothar began his IT career in 1989 at McDonnell Douglas in the Health Software Division,  with Pick/Reality and the “Homer” system. In a varied career of many years, he worked on library systems, banking systems, military and intelligence systems, and was an early user of AI image recognition methods to determine the positions of trains in the Swiss Federal Railways (SBB). He is curious about new languages and frameworks, such as Rust and Nim.

Contact: lothar.joeckel@gmail.com

Credits

1. Visualization of Nimber products of powers of 2. Copyright Watchduck and licensed under the Creative Commons Attribution 3.0 Unported license.
2. Blog roll picture of Nim game. Copyright Uncopy and licensed under the Creative Commons Attribution 3.0 Unported license.

We thank Alex Woodhead for his first blog post, and hope there are many more to follow. If you would like to post on the YottaDB blog please contact us at info@yottadb.com.

TL;DR

Generating patterns for string pattern matching in the M language is expert friendly. An AI tool eases the task. There is a live demonstration where you can try it for yourself.

Using AI to Generate M Patterns

Programs need to determine whether a string matches the pattern for a specific type of data. For example:

Pattern matching allows M (affectionately known as MUMPS) language code to determine whether a string matches a pattern, for example, whether a line of input contains a telephone number, an e-mail address, a date, etc. For example, 1"("3N1")"2(1"-"3N)1N matches a US telephone number in one common format, e.g., (123)-456-7890.

As the syntax of M patterns can be arcane, this blog post describes an AI generative pretrained transformer (GPT) for generating the code for M patterns from natural language input as well as sample datasets.

Patterns can be generated from natural language descriptions or from examples.

Patterns from Descriptions

A natural language description of a telephone number in the format (###)-###-#### where # is a digit could be:

module A
  one of character minus
  followed by three of numeric characters
the main pattern is as follows:
  one of character open-parenthesis
  followed by three of numeric characters
  followed by one of character close parenthesis
  followed by two of module A
  followed by one of numeric character

Natural language descriptions aim to be readable and understandable without technical training. Updating a textual description is more user friendly and less error prone than manual pattern match code. The technical demonstration uses generative AI to transform these text descriptions into M source code code, thus making existing pattern match code accessible for maintenance and support.

Patterns from Samples

Consider the following sample records:

An author of pattern match code may follow a development process:

1. Review the sample records for format features:
   • Numeric sequence length
   • Open and Closing Brackets
   • Hyphen delimiter
2. Decide the characteristics of format rule elements to use.
   • A rule can be defined in different ways.
   • Which approach is elegant, optimal and maintainable?
3. Implement Pattern Match code to encapsulate the format.
4. Test the code against code samples.

What if AI could replace these steps and create pattern match code automatically from the same data samples? The technical demonstration replaces all manual steps 1 through 4 above with automatic code creation.

Finally this technical demonstration closes the development iteration loop for pattern match code by offering:

• Generating natural language descriptions from existing pattern code.
• Generating compliant samples and anti-samples from existing pattern code.

Building Models

In early model training cycles it was found that the features for description-to-pattern were incompatible with samples-to-pattern challenges. Hence development then proceeded to create two separate models, one for each task area.

Description-to-Pattern Model

A synthetic dataset was created consisting of sets of pattern match codes followed by their respective descriptions. An extensive balanced representation, and randomized variation of pattern features was needed to learn:

• Repetition range, e.g., “One-To-Three” numeric characters
• Structure
• Optionality
• Alphanumeric, Numeric, Punctuation
• Literal strings

For model description text, multiple sample variations are introduced to anticipate flexibility for prompt input for example:

• Quantity alias: “two” can be represented in similar words like “Double”, “Twice” and “Pair” or the value “2”
• Character alias: “-” and the word “minus” can be used interchangeably.

By introducing description variation in named written natural languages, a single assimilated model can process prompts in multiple languages.

Samples-to-Pattern Model

Another synthetic dataset was created consisting of sets of sample values followed their respective pattern code. As before an extensive balanced representation, and randomized variation of pattern features was needed to learn:

• Repetition range: “one-To-three” numeric characters
• Structure
• Optionality
• Alphanumeric, Numeric, Punctuation
• Literal strings

Additionally the dataset is balanced for:

1. 1. Generalization
      • Prefer generating exact patterns for small data samples
      • Prefer generating flexible patterns for samples with wide variation
   2. Delimiters, for example:
      • Character "-" in text "(003)-615-2614"
      • Character "^" in text "Smith^Bob^M^19340815"
   3. “Open-Close” pairs features for example:
      • "(" and ")" in text "(003)-615-2614"
      • "<" and ">"

To clarify and elaborate on how the term “generalization” is being used here: In model training there are the concepts overfitting and underfitting. If a model is over trained on a sample dataset it does not perform well for future tasks on new samples not in the original training dataset. There is a similar scenario for the completed PatternMatch model. The model needs to suggest from a small number of sample records, the likely useful pattern match expected. Some patterns can represent tens of thousands of variations of possible samples. Some patterns are very specific with only a few possible samples that all fit in the prompt supplied to the AI. The training data is deliberately curated to provide a wide and graduated range of exact to more generalized patterns. This appears to imbue a logical “pattern usefulness”.

To postulate how the model may achieve this, consider the next possible generated character when outputting software code. This has a constrained range of possibilities.

• In samples where the association of the next token in sequence is weighted to relate to only a small number of possible tokens, this deep feature then suggests generating patterns with small number of possible samples.
• In samples where the association of the next token in sequence is weighted to relate to a wider variety of possible tokens, this deep feature is cascading to prefer generating more general patterns for large possible samples.

The term generalization is wrapping the spectrum of this behavior. The process is not using a human chain-of-thought reasoning to achieve output, but a more fundamental set of self-taught features, impressed during training.

Samples are required to fit within the context window of the GPT. Randomized variation in the number of sample records within each training item, improves deduction capability at low sample numbers. Empirical benchmarking is used to evaluate performance for Generalization vs Delimiters style patterns.

Within the pipeline, two forms of pattern were tracked, where one is a simplified form. This endows generative behavior with a preference for shorter pattern forms, e.g., consider rule “two numeric followed by two to four numeric” can be more simply expressed as “four to six numeric”.

Training Effort

Environment: Nvidia Cuda A10 GPU on the Huggingface platform.

Description-to-Pattern Model

A full retrain is needed when incorporating each new language.

Samples-to-Pattern Model

As the models are separated by task, it becomes convenient to add new language support to descriptions with a relatively quick turnaround.

Benchmarking

Complete success of a pattern match was defined as its ability to satisfy all of its respective candidate sample records, not just those that fit within a context window.

Overview Benchmark Report

The following table gives examples from benchmark candidates demonstrating partial success. It shows the percentage of sample records successfully matched to generated pattern code.

Column Explanation

• Sample Size – The number records available for test sample. Sometimes a pattern describes less than 31 possible exact matches.
• Context Window – Maximum number of sample records used by model to generate a new pattern.
• Rows matched – This is the number of ALL sample records that were matched by the generated pattern. This may be larger than the actual context window.
• Rows matched – Percentage of Rows Matched divided by Sample Size.

Items 4, 6 and 8 all achieve a match count greater than the context window. When pattern match code is generated from sample values the context window used and proportion of matching is returned and displayed as a comment in the technical demonstration.

Front-end Framework

The technical demonstration employs a Gradio web framework as the web display layer. This fits well with HuggingFace ecosystem and infrastructure choices. It provides both a browser displayable layer and a callable API. The callable API is used by benchmarking scripts to leverage cloud GPUs to effect faster report turnaround. The Gradio framework provides a low code user interface implementation approach, freeing more time to focused on domain specific training challenges. For example, in a Python virtual environment with CUDA available, one can translate from English to French, in just a few lines of code:

from transformers import pipeline
import gradio as gr

pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-fr",device="cuda")
demo = gr.Interface.from_pipeline(pipe)
demo.launch(server_port=8080)

This renders the following fully operational web interface for translation:

The following screenshot shows the more involved demo tool interface utilizing the same Gradio framework:

Legend:

The “tick” and “cross” symbols in the “Good” column, confirm expected sample result for “Match” or “Non-Match” context. The use of “tick” and “cross” was chosen to communicate purpose in a mixed language user interface. The “gear” symbol on buttons is used to convey which parts of the user interface employ generative AI functionality.

Example of user workflow steps for the use-case to adjust an existing pattern match expression:

Internationalization

In order to provide to runtime switchable language in the web interface, the community Python package gradio_i18n was employed. Label values are defined in a static dictionary keyed by language. The fiddliest part in demo was getting column titles updating when language changes.

For description training data, a different approach is needed that also incorporates tracking for:

• Single or Plural quantities.
• Gender, e.g., French has words “un” and “une” for number one.

In Spanish, the plural address differs.

For language specific error and informational messages, Python format strings are employed instead of composition. For example the info message placeholder: “info_X_of_Y_rows_used”:

Application Tricks

Retry and Context

When using a list of values to deduce a pattern, the order of the supplied values can influence the model success. To game this, there is a server-side retry-loop that shuffles the values in the context window. Attempting up to 5 times to find a fully matching pattern as validated against ALL of the sample values. It stops at the first full match.

When only a subset of sample values fit in the context window the returned pattern will include a description of the number of rows of samples used. This gives a hint and opportunity to promote specific rows into the context window for a better inference.

Generated patterns are validated against the whole list of patterns. Where this is a partial success pattern suggestion the number of sample rows that do match will be quantified and displayed in code comment.

Future Opportunities

The YottaDB and GT.M pattern match operator offera an extensibility mechanism. Similar to how pattern match code letters ANP mean Alphanumeric, Numeric and Punctuation respectively. Additional letters can encode context specific meanings. Should there be a commonly accepted usage of extensibility for a particular set of applications, it may be possible to tailor an additional trained model to accommodate these features.

About Alex Woodhead

An M programmer with almost a quarter century of experience, Alex Woodhead has worked on projects relating to clinical information system projects on multiple continents. Now living in Southern California, he is focused on synthetic data pipelines for generative AI for novel tools and automation, and is incubating genput.

Contact: info@genput.com

1. 1. 1. Picture of Hunting Carpet made by Ghyath ud-Din Jami, Wool, cotton and silk, 1542–1543, Museo Poldi Pezzoli, Milan. This file has been identified as being free of known restrictions under copyright law, including all related and neighboring rights.
      2. Blog roll picture generated by OpenArt in response to a prompt by K.S. Bhaskar.
      3. Alan Turing quote from https://mathshistory.st-andrews.ac.uk/Biographies/Turing/quotations/
      4. Other graphics provided by the author.

YottaDB r2.02 includes a number of features and enhancements that make YottaDB easier to use, and more like other Linux programs. For example:

• With `ydbsh`, you can create shebang style scripts with M code.
• SOCKET devices support TLS connections using server certifications that do not require a password, such as those issued by Let’s Encrypt.
• Several optimizations to speed up Boolean operations.

In addition to enhancements and fixes made by YottaDB, r2.02 completes the merging of V7.0 GT.M versions into the YottaDB code base, GT.M V7.0-002, V7.0-003, V7.0-004, and V7.0-005, as described in the Release Notes.

TL;DR: Performance Comparisons has instructions for you build a Docker container that allows you to make a side by side comparison of Redis^Ⓡ,[1] Xider™,[2] and YottaDB^Ⓡ. The image above is a screenshot of Xider and YottaDB outperforming Redis with a 32-process workload.

In 2025, the Journal of Computing Languages (JCL) plans a special issue recognizing 30 years of Lua. Since YottaDB has a native Lua API, we have submitted an article to JCL for that issue. A preprint of that article is on arXiv. The article compares the Redis and YottaDB APIs, and delves deeper into the performance comparison described below.

The choice of workload is important when benchmarking databases.

• A realistic benchmark must perform a large number of accesses, in order to remove timing jitter.
• Accesses must be different types, as real world workloads are not monolithic in their databases accesses.
• The benchmark must run on a variety of computing hardware.
• The benchmark must give consistent results on repeated runs.
• The workload should be simple and understandable.

Computing the lengths of 3n+1 sequences meets these requirements. Given an integer n, the next number in the sequence is (a) n÷2 if n is even, and (b) 3n+1 if n is odd. The Collatz Conjecture, one of the great unproven results of number theory, asserts that all such sequences end in 1, i.e., there are no loops, and no sequences of infinite length. For example:

• 3 → 10 → 5 → 16 → 8 → 4 → 2 → 1
• 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1

Note that the sequences starting with 3 and 13 both meet at 10. If there are two processes, one computing the lengths of 3n+1 sequences starting with 13, and the other those of sequences starting with 3, and if they use a database to store the results as they work, then the process which reaches 10 later can simply use the results of the earlier process which has already computed and stored the results for the rest of the sequence.

The picture[3] is a visualization of the 3n+1 sequences of the numbers through 20,000, illustrating the convergence of the sequences, with all eventually converging on 1.

The problem is simple enough for programs to be easily written in virtually any language. While our intent in our blog post Solving the 3n+1 Problem with YottaDB was to compare programming languages, the programs can also be used to compare databases, and database APIs. In particular, we can compare Redis, YottaDB, and Xider. Xider is an API layer that allows YottaDB to provide applications with a Redis-compatible API, with two options:

• over TCP using the Redis Serialization Protocol (RESP); and
• for databases residing on the same machine as the client, we offer a direct connection to the database engine using Python and Lua APIs. The API is the same as the TCP / RESP API, except that it calls the database engine using our in-process native API calls.

In addition to the above two comparisons, which use the same program for Redis and Xider, there are also Lua, Python, and M programs that directly access the database, i.e., without going through a Redis compatible API layer.

We invite you to compare Redis, Xider, and YottaDB for yourself, and send us your comments.