Cache in front of a slow database ?

 

Should You Front a Slow Database with a Cache?

Most of us have been there: a slow database query is dragging down response times, dashboards are red, and someone says, “Let’s put Redis in front of it.”

I have done it myself for an advertising system that needed response times of less than 30 ms. It worked very well.

It’s a tried-and-true trick. Caching can take a query that costs hundreds of milliseconds and make it return in single-digit milliseconds. It reduces load on your database and makes your system feel “snappy.” But caching isn’t free — it introduces its own problems that engineers need to be very deliberate about.

---

Good Use Cases for Caching

• Read-heavy workloads
  When the same data is read far more often than it’s written. For example, product catalogs, user profiles, or static metadata.

• Expensive computations
  Search queries, aggregated analytics, or personalized recommendations where computing results on the fly is costly.

• Burst traffic
  Handling sudden spikes (sales events, sports highlights, viral posts) where the database alone cannot keep up.

• Low latency requirements
  Some systems have low latency requirements. Clients need a response is say less than 50 ms or client aborts.

---

The Catch: Cache Consistency

The hardest part of caching isn’t adding Redis or Memcached — it’s keeping the cache in sync with the database.

Here are the main consistency issues you’ll face:

1. Stale Data
   If the cache isn’t updated when the database changes, users may see outdated results.
   Example: A user updates their shipping address, but the checkout flow still shows the old one because it’s cached.

2. Cache Invalidation
   The classic hard problem: When do you expire cache entries? Too soon → database load spikes. Too late → users see stale values.

3. Race Conditions
   Writes may hit the database while another process is still serving old cache data. Without careful ordering, you risk “losing” updates.

---

Common Strategies

• Cache Aside (Lazy Loading)
  Application checks cache → if miss, fetch from DB → populate cache.
  ✅ Simple, common.
  ❌ Risk of stale data unless you also invalidate on updates.

• Write-Through
  Writes always go through the cache → cache updates DB.
  ✅ Consistency is better.
  ❌ Higher write latency, more complexity.

• Write-Behind
  Writes update the cache, and DB updates happen asynchronously.
  ✅ Fast writes.
  ❌ Risk of data loss if cache fails before DB is updated.

• Time-to-Live (TTL)
  Expire cache entries after a set period.
  ✅ Easy safety net.
  ❌ Not precise; stale reads possible until expiry.

---

So, Is It Worth It?

If your workload is read-heavy, latency-sensitive, and relatively tolerant of eventual consistency, caching is usually a big win.

But if your workload is write-heavy or requires strict consistency (think payments, inventory, or medical records), caching can create more problems than it solves.

The lesson: don’t add Redis or Memcached just because they’re shiny tools. Add them because you’ve carefully measured your system, know where the bottleneck is, and can live with the consistency trade-offs.

---

Takeaway:
Caching is like nitrous oxide for your system — it can make things blazing fast, but you need to handle it with care or you’ll blow the engine.

The Unsung Heroes Behind Your AI Coding Assistant

While everyone's talking about ChatGPT and tools like Cursor, Windsurf, and GitHub Copilot transforming how we code, let's shine a light on the specialized models that actually power these coding experiences.

Meet the Code Generation Champions:

• StarCoder - Trained on 80+ programming languages from GitHub repos, this open-source model excels at code completion and generation

• CodeT5 - Google's encoder-decoder model that understands code structure and can translate between languages

• InCoder - Meta's bidirectional model that can fill in code gaps, not just complete from left to right

• CodeGen - Salesforce's autoregressive model trained on both natural language and code

• Codex (OpenAI) - The foundation behind GitHub Copilot, though now evolved into GPT-4 variants

What makes these different from general LLMs?

• Trained on massive code repositories (billions of lines)
• Understand syntax, semantics, and programming patterns
• Can maintain context across entire codebases
• Specialized in code-specific tasks like debugging, refactoring, and documentation

The magic isn't just in having "AI that codes" - it's in having models that truly understand the intricacies of software development. They aren’t just regurgitating text—they’re tuned for the nuances of programming, which makes them invaluable for developers. These specialized architectures are why your AI assistant can suggest that perfect function name or catch that subtle bug you've been hunting for hours.

The real game-changer? Most of these models are open-source, democratizing access to powerful coding assistance beyond just the big tech companies.

JDK 21 Virtual threads: The end of regular threads ? Not quite.

 A question I get asked all the time: If JDK 21 supports virtual threads, do I ever need to use regular threads ?

Java 21 brought us virtual threads, a game-changer for writing highly concurrent applications. Their lightweight nature and massive scalability are incredibly appealing. It's natural to wonder: do we even need regular platform (OS) threads anymore?

While virtual threads are fantastic for many I/O-bound workloads, there are still scenarios where platform threads remain relevant. Here's why:

1. CPU-Bound Tasks:

Virtual threads yield the carrier thread when they perform blocking I/O operations. However, for purely CPU-bound tasks, they don't offer a significant advantage over platform threads in terms of raw processing power. In fact, the context switching involved might introduce a tiny bit of overhead.

Consider a computationally intensive task like calculating factorials:

Virtual threads example:

// A CPU-intensive task
Runnable cpuBoundTask = () -> {
    long result = 1;
    for (int i = 1; i <= 10000; i++) {
        result *= i;
    }
    System.out.println("Virtual thread task finished.");
};

// Start a virtual thread for the task
Thread.startVirtualThread(cpuBoundTask);

Platform threads example:

Runnable cpuBoundTask = () -> {
    long result = 1;
    for (int i = 1; i <= 10000; i++) {
        result *= i;
    }
    System.out.println("Platform thread task finished.");
};

// Start a regular platform thread
new Thread(cpuBoundTask).start();

For sustained CPU-bound work, managing a smaller pool of platform threads might still be a more efficient approach to leverage the underlying hardware.

2. Integration with Native Code and External Libraries:

Some native libraries or older Java APIs might have specific requirements or behaviors when used with threads. Virtual threads, being a newer abstraction, might not be fully compatible or optimally performant with all such integrations. Platform threads, being closer to the operating system's threading model, often provide better compatibility in these scenarios.

3. Thread-Local Variables with Care:

While virtual threads support thread-local variables, their potentially large number can lead to increased memory consumption if thread-locals are heavily used and store significant data. With platform threads, you typically have a smaller, more controlled number of threads, making it easier to reason about thread-local usage. However, it's crucial to manage thread-locals carefully in both models to avoid memory leaks.

4. Profiling and Debugging:

The tooling around thread analysis and debugging is more mature for platform threads. While support for virtual threads is rapidly improving, there might be cases where existing profiling tools offer more in-depth insights for platform threads.

5. Backward compatibility

If you want you library or server to be available to users who are on JDKs earlier than JDK21, then you have no choice but to use regular threads. Virtual threads are not just a new library; they are a fundamental change to the Java Virtual Machine's threading model (part of Project Loom). The underlying code that manages and schedules virtual threads on top of carrier threads is not present in older JVMs. This can be one of the most important reasons for using platform threads.

In Conclusion:

Virtual threads are a powerful addition to the Java concurrency landscape and will undoubtedly become the default choice for many concurrent applications, especially those with high I/O. However, platform threads still have their place, particularly for CPU-bound tasks, legacy integrations, and situations requiring fine-grained control over thread management.

Understanding the nuances of both models will allow you to make informed decisions and build more efficient and robust Java applications.

Understanding Isolation levels vs Consistency levels

In databases, the terms isolation level and consistency level/model are sometimes used interchangeably. "Read repeatable" and "Serializable" are well known isolation levels. But "Strict Serializable" and "Linearizable" are consistency terms. 

If you have used Mysql or Postgresql, you know probably know what an isolation levels like "Read repeatable" or "Serializable" means. But when you work on a distributed database you hear about consistency level much more.

The first time I heard about consistency level was when I worked with Apache Cassandra which claimed to only support "eventual consistency".  A few years ago when my company was evaluating distributed databases, we had a few architects that insisted that we needed a database that support "strict serializability". CockroachDB was a database that supported this consistency level.

If you are confused, read long. I wrote this blog in attempt to clear up my confusion.

So far, the best explanations on this topic that I found are by Daniel J Abadi [2] [3]. Kyle Kingsbury @Jepson [1] has good descriptions of the topic as well.

But first, a clarification on what consistency means.

What is consistency ?

Consistency is an overloaded term and its meaning has changed in recent times.

ACID consistency

The database must preserve its internal correctness rules after every transaction.

Consider a banking database with a constraint account_balance > 0. 

If the starting account_balance is 50 and a transaction tried to deduct 100, that is a violation of that constraint and should fail.

This is the C is ACID. Databases support constraints to ensure this. But it is mainly the responsibility of the application programmer. It is well understood and rarely discussed these days.

Distributed systems consistency

The system must ensure that all nodes (or clients) agree on the same view of data.

Make the distributed system feel like a single threaded single node system. Read of a value any where in the system produces the same result [2]. The result returned is the most recently written value no matter where it was written.

Consider a system with multiple nodes. X was 1. The value X=2 is written to one node and replicated to others. If clients read from the replicas. Do they all see X=2 immediately ? With strict serializable consistency level , the answer is yes. With weaker models, it is possible they read a an older value. 

Most of us first heard of this description from the CAP theorem.

Why the difference ?

Both describe behavior under concurrency. 

Isolation levels describe problems that occurs in single node databases when transactions execute concurrently. At the highest isolation level transactions execute in some order. Each transaction executes as if it were alone.

In distributed systems there is network latency, replication and partitioning,  all contributing latency and timing issues to concurrency issues. Consistency approaches concurrency issues taking time and latency into account as well. At the highest consistency level, transactions execute in order of their order of completion (commit) in real time. 

Serializable is the strictest isolation level. Strict serializability is the strictest consistency model. In a single node system, there is very little difference between the two because the time issues are small.

Isolation Levels vs. Consistency Models

To summarize the key differences.

Isolation Levels

• Prevent read, writes of uncommitted data.
• Prevent anomalies like read uncommitted, non repeatable reads, phantom reads
• Focus on managing concurrent access to data while balancing performance and correctness.
• Common isolation levels (from weakest to strongest):
  • Read Uncommitted
  • Read Committed
  • Repeatable Read
  • Serializable — the strictest standard defined by the ANSI SQL standard.
    
• Old blog

Consistency Levels

• Typically relevant distributed databases.
• Time is a factor
• They describe the guarantees about visibility and ordering of updates in a distributed, replicated data system.
• They focus on the behavior perceived by clients across multiple nodes or replicas.
• Examples include:
• Strict serializability
• Linearizability 
• causal consistency

Example to Illustrate the Difference:

Scenario:

• Two accounts A and B initially have a balance of 100 each.
• Two concurrent transactions:
  • Tx1: Transfer 50 from A to B.
  • Tx2: Reads balances of A and B and sums them

Isolation level Serializable:

• Tx1 and Tx2 are serialized, and the sum read by Tx2 is 200.
• (Tx1, Tx2) and (Tx2, Tx1) are valid orders irrespective of when each actually committed first.

Consistency level Strict Serializable

• If Tx1 commits before Tx2 starts, Tx2 must see all effects of Tx1. The only valid order is (Tx1, Tx2)
• However if there is some overlap like if Tx1 commits after Tx2 starts, then both orders (Tx1, Tx2) and (Tx2, Tx1) can be valid. Reason is that Tx2 cannot read the data committed by Tx1

A few descriptions

Let us briefly touch on some levels you will encounter often. For more detailed descriptions, I will refer you to https://jepsen.io/consistency [1]

Serializability

Transactions occur in some total order. Even though they may actually execute concurrently, it appears as if they execute one after another. While serializable will prevent non repeatable reads and phantom reads, It will allow "time travel" anomalies as shown in the example above. It can appear that Tx2 happened before Tx1, even though in reality it was the other way around.

Strict Serializability

Transactions occur in a strict order that is consistent with the real time (clock time) order in which transactions occur. It applies to the entire system encompassing multiple objects. A is before B in the order if A commits before B begins. So the only valid order is (A,B). However if A commits after B begin, then both orders (A, B) and (B, A) are valid. 

Linearizable

Transactions occur in a strict order that is consistent with the real time (clock time) order in which transactions occur. But this applies to a single object not to the entire dataset. Definition of a single object varies. Could be a key or a table. [1]

Most of the time concurrency issues are important when multiple threads touch the same data and that why this model is also as important as strict serializability.

Causal Consistency

Transaction that are causally related are seen by all nodes in the same order, while concurrent (unrelated) operations may be seen in different orders. In a social media application, a user making a post and another user liking the post are causally related. The like must be seen only after the post is seen. However, it is ok for a unrelated post that happened after the previous post to be seen before that.

Conclusion

It is all about how systems behave under concurrency. 

Isolation levels deal with how transactions behave when they run at the same time, while consistency models talk about how different nodes in a distributed system agree on data. And "consistency" itself has changed over time, from enforcing business rules in ACID databases to ensuring replicas don't drift apart in distributed ones. 

Database vendors advertise the consistency level they support as a key feature. That is why it is important we understand what it means and ensure that we pick the right database the fits our needs.

References 

1. https://jepsen.io/consistency

2. Introduction to consistency levels , Daniel J Abadi

3. Isolation levels vs Consistency levels, Daniel JAbadi

A non trivial concurrency example in Go language

Overview

In this blog I describe a non trivial concurrency example in the Go programming language. The code is part of my Dynago project https://github.com/mdkhanga/dynago.

This is not a tutorial on Go or on writing concurrent programs. But if you know a little bit of Go and/or little bit of concurrent programing, then I am hoping this can be a useful example. 

In Dynago, I have so far built a leaderless cluster of peer servers. The command for starting a server needs to only point to one other server. The only exception is the first server, which has nothing to point to.  The servers exchange ping and gossip like messages to share the details on the members of the cluster. After a few messages every server has the cluster membership list. When servers join or leave the cluster, the membership list is updated.

Code

If you would like to skip reading the text and jump to the code, the relevant files are :

https://github.com/mdkhanga/dynago/blob/main/cluster/peer.go
https://github.com/mdkhanga/dynago/blob/main/cluster/ClusterService.go

The relevant tests are 
https://github.com/mdkhanga/dynago/blob/main/e2e-tests/test_membership.py
https://github.com/mdkhanga/dynago/blob/main/e2e-tests/test_membership_chain.py

I show some screenshots of code snippets in this blog for the casual reader. But if you are interested in the code, I recommend directly looking at the code in github.

Go concurrency recap

In Go, you write code that executes concurrently by writing Goroutines.

A goroutine is like a lightweight thread. It is written as a regular function.

You run a goroutine using the go keyword.

Go provides channels for communicating between goroutines. Channels are a safer way to send and receive data. This is safer than using shared memory as is done in Java, C++. You do not have to use mutexes to avoid memory visibility and race conditions.

The select statement lets the goroutine wait on receiving data on a channels.

Though not recommended, the shared memory approach of  data exchange is also supported. You will need to use primitives from the https://pkg.go.dev/sync package to synchronize access to data.

The Examples

There were two features in Dynago where concurrency is relevant.

1. Each server receives a message on a GRPC stream. It has to process the message and sometimes send a response back. This is the classic producer - consumer problem. Some goroutines produce. Other goroutines consume and do work. I use channels for sharing the messages between producers and consumers.

2. A map stores the list of cluster members. 

• The map is to be updated as servers join or leave the cluster.
• Periodically we need to iterate over the map and send out our copy of the membership list to others.
• The receiving server will merge the received list with it own list.
• If the receiving servers list is more up to date, then it sends a response to and the original sender has to merge.
• timestamps are used to determine who is more recent

This the case of multiple goroutines reading and writing a data structure concurrently. 

Example 1: Channels 

The peer struct shown in the code below defines a channel InMessagesChan for incoming messages and a channel OurMessgesChan for outgoing messages.

The receiveMessages goroutine method has for loop with following code. It reads a message from a Grpc stream and sends it to the InMessagesChannel.

The processMessageLoop goroutine method in peer.go has a loop that takes messages from the InMessagesChannel and processes them.  When a response is needed, it processes the message and writes the response that needs to go out to the OutMessagesChannel. The image below is shortened version of the real code.

Lastly the sendLoop goroutine method has a for loop that takes messages from the outMessageChannel and writes them to the outbound Grpc stream.

As you can see, very simple. 3 goroutines working concurrently by exchange data over channels. No locking, no synchronization an fewer problems.

In my opinion, channels is one of the best features of Go.

Example 2 : Shared memory

I have this struct cluster which has the list of peers in the cluster

We need to add /update / remove entries from the map in a thread safe manner. The code below uses the mutex to synchronize access to the map. These methods are either called from both peer.go and cluster.go.

The code below shows a loop from the ClusterInfoGossip method that uses the same mutex to synchronize the map while iterating over it. Typically the calls from add, remove and gossip happen from different goroutines. If you do not synchronize, you will have memory visibility problems. Note that since peer is updated, we need to synchronize access to the peer as well.

You might be wondering, why can I not just do this using channels when it is safer and cleaner ? What you would do is to write a function with like an event loop.  The function can accept commands on a channel and read , update or iterate over the map based on the command. The code would look something like below

Which approach you take is sometimes a personal choice. For the message processing, I preferred channels. But for CRUD on a map, I preferred shared memory.

Conclusion

In conclusion, what I have shown you here is a non trivial concurrency example in Go. Go makes it quite easy to write concurrent programs. The recommended way to share data between goroutines is by channels. By using channels, you can avoid race conditions and memory visibility issues. But the traditional approach of shared memory with synchronization is also supported. 

As I build out Dynago, I plan to blog about the interesting pieces.  If you like my blogs, please follow me on LinkedIn and/or X/twitter.

Multi Version Concurrency Control (MVCC) in databases

Introduction

Multi version concurrency control (MVCC) is a popular optimistic technique used in modern databases for concurrency control.

MVCC does not use locking. In that regard it is an optimistic technique but distinct from what is know as the optimistic concurrency control (OCC).

MVCC is timestamp based.

MVCC does its concurrency control by keeping multiple copies or versions of each data item. Every transaction sees data only of a specific version , also known as snapshot. Changes made by a transaction will not be seen by others, until the changes are committed.

Concepts

Versioning

Versioning tuples is at the core of how MVCC does its concurrency control.

Consider the table 

id, balance

A, 500

B, 1000

Now logically assume that under the hood, the database adds 2 hidden columns - start time (st) and end time (et)

id, balance, st. , et

A, 500 , 1 , -

B, 1000, 2, -

Let us assume that increasing numbers 1,2 ..... represent time and a - implies infinity or no end time. The version number can be a timestamp or something analogous to a timestamp.

In the above example, we are saying that the first tuple (A, 500) is valid from time 1 to infinity. The second tuple (B, 1000) is valid from 2 to infinity.

With versioning, when a tuple is updated, the original one is unchanged. Instead a new row is added.

Let us say at time 3, A is updated to 550. Logically the table would look like

A, 500 , 1 , 3

A, 550 , 3 , -

B, 1000, 2, -

The latest committed version has an end time of infinity. When a new update for a tuple is committed, the end timestamp of the previous latest is changed to the start timestamp of the new latest.

A transaction reads the latest committed version at the time it starts. A transaction that starts at time 2 and reads A will read 500. But a transaction that starts at 4 and reads A will read 550.

Snapshot isolation

Every transaction only reads committed values at the time it starts. It is a snapshot of the database at that time. This referred to as Snapshot isolation.

However it will read any changes that it makes within the scope of its transaction.

Use cases

It is easier to understand MVCC with some examples,

Lost update problem

Let us start with the same table again.

id, balance, st. , et

A, 500 , 1 , -

At time 2, transaction T1 reads A. The latest version with timestamp (1, -) has value 500. It reads 500.

At time 3, transaction T2 reads the same row. The latest version is still (1, -) . It too reads 500.

At time 3, transaction T2 updates the value to 550 and commits. We now have an addition version

A, 500 , 1 , 3

A, 550, 3 , - 

At time 5, T1 updates the value to 600. 

At time 6, T1 tries to commit. 

T1s commit is disallowed and it is forced to abort. 

The reason is that T1's snapshot is of time 1. After T1 started, another transaction came along , performed a WRITE on the same record and committed. If we allowed T1 to also commit, it would overwrite T2's update. This is the lost update problem.

The rule we can deduce here is : The write set of the committing transaction T1 should not intersect with the write set of any transactions that committed after T1 started and before T1 commits.

Write Skew

Let us start this time with the table having 2 tuples.

id, balance, st. , et

A, 500 , 1 , -

B, 1000, 2, -

At time 1, transaction T1 reads A. The latest version with timestamp (1, -) has value 500. It reads 500.

At time 3 , transaction T1 writes A to 600. But it is still uncommitted.

A, 500 , 1 , -, 

A, 600 , 3 , - , uncommitted

B, 1000, 2, -

At time 4, transaction T2 reads A. The latest committed record is still at timestamp 1. It will read 500 ( not 600 ).

At time 5, T1 commits. The write set to T1 does not intersect with the write set of any other transaction. So it allowed to commit. So we have

A, 500 , 1 , 5, 

A, 600 , 5 , - , 

B, 1000, 2, -, 

At time 6, T2 updates B to 1200

B, 1200, 6, -, uncommitted

At time 7, T2 tries to commit. It is disallowed and T2 is forced to abort. The reason is that T2 read A which was committed after T2 started. Even though T2 does not update A. It might be using A (that is read earlier) to update say B and that update could be incorrect.This is the write skew problem.

We can thus deduce rule 2: The read set of a committing transaction T should not intersect with the write set of any transaction that has committed after T started.

Phantom reads

Let us look at a case where a new record is insert into the table.

id, balance, st. , et

A, 500 , 1 , -

B, 1000, 2, -

At time 3 , transaction T1 runs the query "select sum(balance) from table where balance >=500 ". It gets a result 1500.

At time 4, transaction T2 inserts a new row, (C, 600) and commits

id, balance, st. , et

A, 500 , 1 , -

B, 1000, 2, -

C, 600, 4 , -

At time 5, transaction T1  inserts the 1500 value it read into another table.

At time 6, T1 tries to commit. The transaction is not allowed to proceed and aborts.

The reason is that the 1500 value is no longer accurate. At time 4 , another transaction committed a new row and the value should be 2100. 

Rule 3 : If the committing transaction T has predicate queries  (where clause ) that depend to number of rows and affected by inserts/deletes, then just before committing the queries need to be run again. The commit is allowed only if the results are the same as before.

Obviously running the queries a second time can be expensive. But that is implementation detail and databases do various optimization.

Garbage collection

Maintaining versions for each and every record can take up a lot of disk space. Databases have background process or other method to remove old versions that have no active transaction depending on them.

Algorithm

Every tuple is versioned using timestamp or something analogous to it.

Every transaction only reads rows that are committed as of the start time of the transaction. This holds for the entire duration of the transaction. It cannot read any commits that happened after it starts. In other words it sees a "Snapshot" of the data, as of its start time. This is Snapshot isolation.

When a transaction tries to commit, we check if any other transaction has committed a new version for the rows that we read or write. If there are any such transactions, then we must abort.

Even if our read/write set does not intersect with any other transaction, if our read is impacted by inserts or deletes, we much abort.

To make this happen, the database needs to maintain start time, end time for every tuple. Not just the latest version, but older versions as well. 

For every tuple that is read or writen, the database needs to know which transactions are active and which committed.

Older versions need to cleaned up when there are no active transactions depending on them.

For a transaction to be allowed to commit, there rules we discussed apply:

#1 : Write set of a committing transaction T should not intersect with Write set of any committed transaction that committed after T started.

#2: The Read set of a committing transaction T should not intersect with the write set of any transaction that has committed after T started.

#3: When the committing transaction T has predicate queries (WHERE clause, depend on number of rows meeting condition ), then before committing, the queries need to be rerun to ensure that they return the same result.

Some additional rules:

#4 Write set of a committing transaction T should not intersect with Read set of any transaction that committed after T started.

#5 If a transaction T updates a row, T should use its own updated version, not the one in older snapshot it started with.

Commercial Databases supporting MVCC

Almost all the modern databases we know off support MVCC.

PostgreSql

Mysql

Oracle

CockroachDB

SqlServer

.... and more

Advantages of MVCC

• Readers do not block readers. Writers do not block readers.
• Each transaction works under a consistent snapshot of data.
• High concurrency is possible.
• Databases can allow querying of old versions, which are known as time travel queries.

Disadvantages of MVCC

• Increased storage requirement because we are storing older version.
• Need to do garbage collection.
• When conflicts are detect and transactions aborted, applications need to retry.

Conclusion

MVCC is a popular concurrency control technique used in many currently popular databases like Postgresql.

It does not use two phase locking. Unlike OCC, it does not use a staging area. The staging area is built in with versioning.

Like OCC, it works best for workloads where there are few conflicts.

In conclusion, Multi-Version Concurrency Control (MVCC) offers a robust approach to handling concurrent transactions in modern databases by maintaining multiple versions of data. 

While it provides significant benefits such as high read throughput, reduced contention, and snapshot isolation, it also introduces complexity in garbage collection and storage management.

Modern distributed databases combine versioning with clock time to provide strict serializability even in  distributed databases without the use of locking. But that is a topic for another blog.

References

Prof. Jen Dittrich Youtube videos

Andy Pavlov CMU Database Youtube video

Database Internals by Alex Petrov

Database Management Systems by Ramakrishnan & Gehrke

Optimistic Concurrency Control In Databases

Introduction

Optimistic concurrency control refers to concurrency control in databases without the use of explicit locks. Traditionally it is done using locks.  But locking reduces throughput and scalability. Modern databases therefore prefer not to use locking and use optimistic techniques. This is a brief description of the optimistic technique.

Why concurrency control ?

Concurrency control in databases refers to techniques used to execute transactions concurrently.

From a end users standpoint, every transaction needs to execute correctly and preserve the consistency of the database, irrespective of what other transactions are doing. For the user, it should appear that the transactions are executing in some serial order.

When multiple transactions read and/or write the same data several kinds of errors can happen such as

• dirty reads - reading uncommitted data
• non repeatable reads - read a second time and you get a different value
• phantom reads - read a second time and you get additional rows
• Lost update - one transaction overwrites others write
• dirty write - a transaction reads an uncommitted value and updates it
• write skew - the combination of two transactions breaks some invariant like minimum balance

At a user level, databases give the users a knob called the isolation level to protect against some of these errors. For an explanation of isolation levels, see the blog I wrote many a few years ago.

But under the hood databases implement concurrency control to ensure that users see a consistent view of the data at all times.

There are 3 main types of concurrency control:

• Pessimistic concurrency control
• Optimistic concurrency control (OCC) 
• Multi version concurrency control (MVCC)

The pessimistic approach involves locking resources - rows, tables , pages etc while they are used by one transactions. Other transaction that need that resource wait until the lock is released. This approach prevents conflicts but comes at the cost of throughput.

OCC and MVCC take the optimistic approach that conflicts rarely happen. They lets transactions proceed without acquiring locks. If a conflict is detect downstream, one of the transaction is forced to abort.

In this blog we discuss one such concurrency control technique - optimistic concurrency control. I will discuss the other two in subsequent blogs.

What is optimistic concurrency control ?

As the name suggests, the approach is optimistic. Optimistic implies a positive view of things that most transactions do not conflict with other and there is no need to slow things down by acquiring locks and holding on to them. 

Assumptions are that

• Transactions are short lived
• They rarely conflict

The concurrency control here is done in 3 stages.

In the first stage (read) the transaction executes its operations in a private or staging area. Rows that are read known as the read set and rows that are updated known as the write set are identified.

In the second stage (validation) the operations are checked for conflicts. Rows that are being read by one transaction should not be be updated by others. If a conflict is detected, then the transaction is aborted and the staging area cleared

In the third stage, if there are no conflicts the changes are committed.

Detecting the conflicts

Given 2 transactions A and B, 

A and B are given timestamps(TS) before the validation starts.

Let assume TS (A)  < TS (B).

If A committed before B started read, then there are no issues and B is also allowed to commit.

If A committed after B started read but before B started write, then both can commit if the write set of A does not intersect with the read set of B.

If A read completes before B read completes, and the write set of B does not intersect with both the read set of A and write set of A, then both can commit.

In all the 3 cases, A and B are operating on different records and do not conflict.

In this example, we are looking "backward" from B. If there is a conflict B will asked to abort. Some databases look "forward" but the concept is the same.

When a validation is being done, no other transaction is allowed to commit. Only one transaction is validated at a time.

Timestamp based

In pessimistic concurrency control, transactions are ordered in the order in which locks are acquired.

In optimistic concurrency control, transactions are ordered based on timestamps given to transactions.

Commercial databases using OCC

Some example are:

Amazon Aurora DSQL

Yugabyte

CockroachDB

FoundationDB

Fauna

Google Spanner

Advantages of OCC

Higher scalability

Higher throughput

No deadlocks

In general this is suitable for low conflict data access.

Disadvantages

Transactions might need to be aborted.

Applications need to handle aborted transactions and retry.

This might not be suitable for cases when conflict is high - that is multiple transactions needs to access the same data.

Conclusion

Optimistic concurrency control is becoming increasing popular is modern databases that need to scale and can be distributed across multiple node. It really works well in low data contention scenarios. It is ideal for distributed databases where the locking approach becomes even more expensive when you need to lock across nodes - as would be needed in modern distributed databases. For reasons mentioned above, distributed , cloud native, high concurrency systems are moving towards OCC.

References:

1. Database Management Systems - Ramakrishnan and Gehrke

2. Database Internals - Petrov

3. Andy Pavlov CMU lecture on youtube