DEV Community: Mustafa Bingül

Why InfluxDB Is the Go-To Database for Time-Series Data (With Real Examples)

Mustafa Bingül — Tue, 28 Apr 2026 20:08:53 +0000

Why InfluxDB Is the Go-To Database for Time-Series Data 🚀

If you've ever tried to store metrics, IoT sensor readings, application performance data, or financial tick data in a traditional relational database — you know the pain. Tables balloon in size, queries crawl, and your DBA starts giving you the look.

That's where InfluxDB comes in.

In this post, I'll break down:

What makes InfluxDB different from traditional databases
Core concepts you need to know
Real-world use cases
Hands-on code examples (Python & Flux)
When not to use InfluxDB

Let's go. 🏁

🤔 What Is InfluxDB?

InfluxDB is an open-source time-series database (TSDB) built by InfluxData. It is designed from the ground up to handle high-write-throughput workloads where data points are tied to a timestamp.

Think of it as a database that answers questions like:

"What was the CPU usage on server-3 between 2:00 PM and 3:00 PM?"
"Show me the average temperature in Warehouse B over the last 7 days."
"Alert me when request latency exceeds 500ms for more than 2 minutes."

Traditional SQL databases can do this — but they were never optimized for it.

⚡ Why InfluxDB? The Core Advantages

1. Purpose-Built for Time-Series

InfluxDB stores data in a columnar format optimized for time-ordered queries. It handles millions of writes per second with ease, something PostgreSQL or MySQL would struggle with at scale.

2. Automatic Data Compression

InfluxDB uses techniques like run-length encoding and delta encoding for timestamps and values, resulting in drastically smaller storage footprints compared to row-based storage.

3. Retention Policies (Built-In TTL)

You can define how long data lives before it's automatically purged — no cron jobs, no manual cleanup.

4. Flux — A Powerful Query Language

InfluxDB 2.x introduced Flux, a functional data scripting language that makes complex time-series transformations readable and composable.

5. Native Integrations

Out-of-the-box support for Grafana, Telegraf, Prometheus, Kubernetes, and more.

🧠 Core Concepts

Before jumping into code, let's clarify the data model:

Concept	Description	SQL Equivalent
Bucket	Where data is stored (with retention policy)	Database
Measurement	The name of what you're tracking	Table
Tags	Indexed metadata (strings)	Indexed columns
Fields	The actual values being measured	Non-indexed columns
Timestamp	When the data point was recorded	`created_at` column

Example data point in Line Protocol (InfluxDB's native write format):

cpu_usage,host=server-01,region=eu-west value=72.4 1717000000000000000
│           │                             │         │
measurement  tags                          field     timestamp (nanoseconds)

🛠️ Getting Started

Run InfluxDB Locally with Docker

docker run -d \
  --name influxdb \
  -p 8086:8086 \
  -e DOCKER_INFLUXDB_INIT_MODE=setup \
  -e DOCKER_INFLUXDB_INIT_USERNAME=admin \
  -e DOCKER_INFLUXDB_INIT_PASSWORD=supersecret \
  -e DOCKER_INFLUXDB_INIT_ORG=my-org \
  -e DOCKER_INFLUXDB_INIT_BUCKET=my-bucket \
  -e DOCKER_INFLUXDB_INIT_ADMIN_TOKEN=my-super-secret-token \
  influxdb:2.7

Open your browser at http://localhost:8086 — the UI is ready. ✅

🐍 Python Examples

Install the Client

pip install influxdb-client

Writing Data

from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS
from datetime import datetime, timezone

# Connection config
url    = "http://localhost:8086"
token  = "my-super-secret-token"
org    = "my-org"
bucket = "my-bucket"

client = InfluxDBClient(url=url, token=token, org=org)
write_api = client.write_api(write_options=SYNCHRONOUS)

# Write a single data point
point = (
    Point("cpu_usage")
    .tag("host", "server-01")
    .tag("region", "eu-west")
    .field("value", 72.4)
    .field("idle", 27.6)
    .time(datetime.now(timezone.utc))
)

write_api.write(bucket=bucket, org=org, record=point)
print("✅ Data written successfully!")

client.close()

Writing Batch Data (Simulated Sensor Readings)

import random
from datetime import datetime, timedelta, timezone
from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", token="my-super-secret-token", org="my-org")
write_api = client.write_api(write_options=SYNCHRONOUS)

points = []
base_time = datetime.now(timezone.utc) - timedelta(hours=1)

for i in range(60):  # 60 data points, one per minute
    timestamp = base_time + timedelta(minutes=i)
    point = (
        Point("temperature")
        .tag("warehouse", "warehouse-b")
        .tag("sensor_id", "sensor-42")
        .field("celsius", round(20.0 + random.uniform(-2.0, 5.0), 2))
        .field("humidity", round(55.0 + random.uniform(-5.0, 5.0), 2))
        .time(timestamp)
    )
    points.append(point)

write_api.write(bucket="my-bucket", org="my-org", record=points)
print(f"✅ {len(points)} data points written!")

client.close()

Querying Data with Flux

from influxdb_client import InfluxDBClient

client = InfluxDBClient(url="http://localhost:8086", token="my-super-secret-token", org="my-org")
query_api = client.query_api()

# Get average temperature per minute over the last hour
flux_query = """
from(bucket: "my-bucket")
  |> range(start: -1h)
  |> filter(fn: (r) => r._measurement == "temperature")
  |> filter(fn: (r) => r._field == "celsius")
  |> filter(fn: (r) => r.warehouse == "warehouse-b")
  |> aggregateWindow(every: 1m, fn: mean, createEmpty: false)
  |> yield(name: "mean_temp")
"""

tables = query_api.query(flux_query)

for table in tables:
    for record in table.records:
        print(f"[{record.get_time()}] Temp: {record.get_value():.2f}°C")

client.close()

📊 Flux Query Language — Quick Reference

Flux is pipeline-based (similar to Unix pipes or pandas method chaining). Here are the most useful patterns:

Filter by Tag

from(bucket: "my-bucket")
  |> range(start: -24h)
  |> filter(fn: (r) => r._measurement == "cpu_usage")
  |> filter(fn: (r) => r.host == "server-01")

Aggregate Over Time Windows

from(bucket: "my-bucket")
  |> range(start: -7d)
  |> filter(fn: (r) => r._measurement == "cpu_usage")
  |> aggregateWindow(every: 1h, fn: mean)

Detect Anomalies with `movingAverage`

from(bucket: "my-bucket")
  |> range(start: -6h)
  |> filter(fn: (r) => r._measurement == "cpu_usage")
  |> movingAverage(n: 10)
  |> filter(fn: (r) => r._value > 90.0)  // Only values above 90%

Join Two Measurements

cpuData = from(bucket: "my-bucket")
  |> range(start: -1h)
  |> filter(fn: (r) => r._measurement == "cpu_usage")

memData = from(bucket: "my-bucket")
  |> range(start: -1h)
  |> filter(fn: (r) => r._measurement == "memory_usage")

join(tables: {cpu: cpuData, mem: memData}, on: ["_time", "host"])

🌍 Real-World Use Cases

1. Infrastructure Monitoring

Use Telegraf (InfluxData's collection agent) to collect metrics from servers, containers, and network devices — and store them in InfluxDB with zero custom code.

# telegraf.conf snippet
[[inputs.cpu]]
  percpu = true
  totalcpu = true

[[inputs.mem]]

[[outputs.influxdb_v2]]
  urls = ["http://localhost:8086"]
  token = "my-super-secret-token"
  organization = "my-org"
  bucket = "infrastructure"

Run it:

telegraf --config telegraf.conf

2. IoT Sensor Data

Smart factories, weather stations, and agricultural sensors generate continuous streams of data. InfluxDB handles millions of writes per second without breaking a sweat.

3. Application Performance Monitoring (APM)

Track request latency, error rates, and throughput over time — then set up alerts when thresholds are breached.

4. Financial Market Data

Store tick-by-tick price data for instruments. Time-series databases are the industry standard for this use case.

⚠️ When NOT to Use InfluxDB

InfluxDB is fantastic — but it's not a silver bullet. Avoid it when:

You need complex JOINs across non-time-series entities (use PostgreSQL).
Data is mostly static or updated in-place (user profiles, product catalogs).
You need full ACID transactions (use a traditional RDBMS).
You're storing documents or blobs (use MongoDB or S3).

The rule of thumb: if time is the primary axis of your query, InfluxDB is your friend.

🆚 InfluxDB vs. Alternatives

Feature	InfluxDB	TimescaleDB	Prometheus	Grafana Mimir
Native TSDB	✅	❌ (PostgreSQL ext.)	✅	✅
SQL Support	❌ (Flux/InfluxQL)	✅	❌ (PromQL)	❌ (PromQL)
Long-term storage	✅	✅	⚠️ (limited)	✅
Grafana integration	✅	✅	✅	✅
Write throughput	🔥 Very high	High	Medium	High
Self-hosted	✅	✅	✅	✅
Managed cloud	✅ (InfluxDB Cloud)	✅	❌	❌

🏁 Summary

InfluxDB shines when:

✅ You have high-frequency time-stamped data
✅ Write speed is critical
✅ You need efficient range queries over time
✅ Data has a natural expiration (retention policies)
✅ You're building dashboards or alerting systems

The ecosystem around InfluxDB — Telegraf for collection, Flux for querying, and Grafana for visualization — makes it one of the most complete observability stacks available today.

If you're not already using a time-series database for your metrics and monitoring workloads, it's time to make the switch. Your future self (and your DBA) will thank you.

📚 Further Reading

Got questions or feedback? Drop a comment below — happy to chat about time-series databases all day long. ⏱️

I Rebuilt the Core of My Data Engine — Here's What Changed in v1.3.0

Mustafa Bingül — Mon, 27 Apr 2026 16:44:52 +0000

I Rebuilt the Core of My Data Engine — Here's What Changed in v1.3.0

What is Nexus Core?

Nexus Core is a standalone Java application — a central data orchestration engine that sits between your distributed server infrastructure and your persistence layer. All data operations (reads, writes, deletes, increments) are routed through it via a Redis pub/sub message bus. Your servers stay thin. Your data logic lives in one place.

This release focused on three things: live health signaling, intelligent cache management, and thread isolation.

🫀 Live Protocol — Heartbeat Every Second

The most important addition in v1.3.0 is LIVE — a new RequestType in DataAddon that represents Nexus announcing its own presence on the network.

Every second, RedisDataContainer schedules a broadcast to the darkland_nexus_live channel:

// RedisDataContainer.java
rm.scheduleTask(this::sendNetworkLiveBroadcast, 1, 1, TimeUnit.SECONDS);

private void sendNetworkLiveBroadcast() {
    NexusApplication.getApplication().getRedisManager().processTask(() -> {
        NexusJsonDataContainer jsonDataContainer = new NexusJsonDataContainer();
        jsonDataContainer.set("type", "LIVE");
        jsonDataContainer.set("source", "nexus");
        jsonDataContainer.set("time", System.currentTimeMillis() / 1000L);

        NexusApplication.getApplication().getRedisManager()
            .publish("darkland_nexus_live", jsonDataContainer.toFullJson());
    });
}

Any subscriber listening to darkland_nexus_live can now verify that Nexus is alive before sending requests. If the heartbeat stops, clients know the engine is down and can react accordingly — no more firing requests into silence.

The LIVE type is also formally part of the RequestType enum in DataAddon:

public enum RequestType {
    SET_DATA, GET_DATA, UPDATE_DATA, REMOVE_DATA,
    BROADCAST, LOAD_CACHE, INCREMENT_DATA, LIVE
}

🗄️ Cache — Dynamic TTL per DataAddon

Previously there was no way to define per-module cache lifetimes. v1.3.0 adds an abstract method to DataAddon:

public abstract int getCacheTTL();

Every addon now declares its own TTL. When a key is written to Redis, that TTL is applied directly:

// RedisManager.java
public void setData(String key, String json, DataAddon addon) {
    processTask(() -> {
        try (Jedis jedis = pool.getResource()) {
            SetParams params = new SetParams().ex(addon.getCacheTTL());
            jedis.set(key, json, params);
        }
    });
}

And on every cache hit, the TTL is renewed (touch-to-renew / sliding expiration):

// DataAddon.java — inside getData()
if (app.getRedisManager().exists(keyTag)) {
    String redisJson = app.getRedisManager().getData(keyTag).get();
    DataModel m = new DataModel(keyTag, UUID.randomUUID().toString(),
            modelInitComp(redisJson), this, specificValue);
    app.getDataContainer().addModelFix(keyTag, m);
    app.getRedisManager().renewTTL(keyTag, getCacheTTL()); // sliding expiration
    return Optional.of(m);
}

// RedisManager.java
public void renewTTL(String key, int seconds) {
    processTask(() -> {
        try (Jedis jedis = pool.getResource()) {
            jedis.expire(key, seconds);
        }
    });
}

Active data stays warm. Unused data evicts itself. No manual intervention needed.

🔄 L1 / L2 Auto-Invalidation via Keyspace Notifications

When Nexus connects to Redis, it automatically enables keyspace notifications:

// RedisManager.java
public void enableKeyspaceNotifications() {
    try (Jedis jedis = pool.getResource()) {
        jedis.configSet("notify-keyspace-events", "Ex");
        System.out.println("Nexus: Keyspace Notifications (Expired) enabled via Jedis.");
    }
}

A dedicated thread (Nexus-L1-Sync-Thread) subscribes to the expiry channel and immediately purges the in-process L1 cache when a key expires in Redis:

// RedisDataContainer.java
public void startL1SyncListener() {
    new Thread(() -> {
        String expiredChannel = "__keyevent@0__:expired";
        while (!Thread.currentThread().isInterrupted()) {
            try (Jedis jedis = pool.getResource()) {
                jedis.subscribe(new JedisPubSub() {
                    @Override
                    public void onMessage(String channel, String message) {
                        removeModel(message);
                        LOGGER.warning("[Nexus] Key expired: " + message
                            + ", Removing from L1 cache to maintain data integrity.");
                    }
                }, expiredChannel);
            } catch (Exception e) {
                try { Thread.sleep(5000); } catch (InterruptedException ie) { break; }
            }
        }
    }, "Nexus-L1-Sync-Thread").start();
}

The result: L1 (RAM) and L2 (Redis) are always in sync. Stale reads are gone.

⚡ Dual-Queue Worker Architecture

Before v1.3.0, inbound messages and outbound tasks competed on the same thread pool. v1.3.0 separates them completely with two independent LinkedBlockingQueue instances:

// RedisManager.java
private final BlockingQueue<String>   messageQueue      = new LinkedBlockingQueue<>(50000);
private final BlockingQueue<Runnable> internalTaskQueue = new LinkedBlockingQueue<>(50000);

Inbound workers (one per CPU core) pull from messageQueue and handle incoming Redis messages:

private void startInboundWorkers() {
    int cores = Runtime.getRuntime().availableProcessors();
    for (int i = 0; i < cores; i++) {
        new Thread(() -> {
            NexusReceiver receiver = new NexusReceiver(this);
            while (!Thread.currentThread().isInterrupted()) {
                try {
                    String msg = messageQueue.take();
                    receiver.handleSyncMessage(msg);
                } catch (InterruptedException e) {
                    Thread.currentThread().interrupt();
                    break;
                }
            }
        }, "Nexus-Inbound-Worker-" + i).start();
    }
}

Outbound workers pull from internalTaskQueue and handle Redis/Mongo write operations:

private void startOutboundWorkers() {
    int workers = Math.max(2, Runtime.getRuntime().availableProcessors());
    for (int i = 0; i < workers; i++) {
        new Thread(() -> {
            while (!Thread.currentThread().isInterrupted()) {
                try {
                    Runnable task = internalTaskQueue.take();
                    task.run();
                } catch (InterruptedException e) {
                    Thread.currentThread().interrupt();
                    break;
                }
            }
        }, "Nexus-Outbound-Worker-" + i).start();
    }
}

An inbound spike can no longer delay outbound responses. If the outbound queue is full, the task falls back to the ScheduledExecutorService:

public void processTask(Runnable task) {
    if (!internalTaskQueue.offer(task)) {
        scheduler.execute(task); // fallback
    }
}

🔍 Reflection Caching

DataAddon uses annotations (@DbDataModels) to define its schema. Previously these fields were scanned via reflection on every request. v1.3.0 caches the result with double-checked locking:

// DataAddon.java
private volatile Field[] cachedAnnotatedFields = null;
private final Object fieldCacheLock = new Object();

private Field[] getAnnotatedFields() {
    if (cachedAnnotatedFields != null) return cachedAnnotatedFields;
    synchronized (fieldCacheLock) {
        if (cachedAnnotatedFields != null) return cachedAnnotatedFields;
        cachedAnnotatedFields = java.util.Arrays.stream(getClass().getDeclaredFields())
                .filter(f -> f.isAnnotationPresent(DbDataModels.class))
                .peek(f -> f.setAccessible(true))
                .toArray(Field[]::new);
    }
    return cachedAnnotatedFields;
}

Same pattern for the ID field — scanned once, cached for all subsequent requests:

private volatile String cachedIdFieldName = null;
private volatile Class<?> cachedIdClass = null;
private final Object idCacheLock = new Object();

Reflection runs once per addon class. Every request after that hits the cache directly.

Architecture Summary

Distributed Servers
  Server #1 ─┐
  Server #2 ──┼──► darkland_nexus (Redis) ──► Nexus-Inbound-Worker-N
  Server #3 ─┘                                        │
                                              NexusReceiver.handleSyncMessage()
                                                        │
                                              Nexus-Outbound-Worker-N
                                                        │
                                         ┌──────────────┴──────────────┐
                                         │  L1 Cache (ConcurrentHashMap)│
                                         │  L2 Cache (Redis + TTL)      │
                                         │  MongoDB (persistence)       │
                                         └─────────────────────────────┘
                                                        │
                            darkland_nexus_live ◄── heartbeat every 1s

Source → github.com/mustafabinguldev/nexus-core

Java Reflection API: Inspect & Manipulate Code at Runtime

Mustafa Bingül — Sun, 26 Apr 2026 19:35:38 +0000

Java Reflection API: Inspect & Manipulate Code at Runtime

A practical deep dive into one of Java's most powerful — and misunderstood — features

Tags: #java #reflection #jvm #tutorial

What is Reflection?
Getting a Class Object
Inspecting Fields, Methods & Constructors
Invoking Methods Dynamically
Accessing Private Members
Annotations via Reflection
Real-World Use Cases
Performance & Pitfalls

1. What is Reflection?

Java Reflection is the ability of a program to inspect and modify its own structure and behaviour at runtime. Through the java.lang.reflect package, you can examine classes, interfaces, fields, methods, and constructors — even if you don't know their names at compile time.

Think of it as a mirror held up to your own code: you can look at what exists, read its metadata, and call it dynamically.

💡 When does it shine? Frameworks like Spring, Hibernate, and JUnit rely heavily on Reflection to wire dependencies, map entities, and discover test methods — all without you writing explicit calls.

2. Getting a Class Object

Every Reflection operation starts with a Class<?> object. There are three main ways to get one:

// 1. From the class literal (compile-time known type)
Class<String> c1 = String.class;

// 2. From an instance at runtime
String s = "hello";
Class<?> c2 = s.getClass();

// 3. By fully-qualified name (most dynamic)
Class<?> c3 = Class.forName("com.example.MyService");

Class.forName() is the most powerful form — it lets you load classes whose names are only known at runtime (e.g. read from a config file or database).

3. Inspecting Fields, Methods & Constructors

Fields

Class<?> clazz = Person.class;

// Only public fields (includes inherited)
Field[] publicFields = clazz.getFields();

// All declared fields (private, protected, package) — no inherited
Field[] allFields = clazz.getDeclaredFields();

for (Field f : allFields) {
    System.out.println(f.getName() + " → " + f.getType().getSimpleName());
}

Methods

Method[] methods = clazz.getDeclaredMethods();

for (Method m : methods) {
    System.out.println(
        m.getName()
        + " | params: " + m.getParameterCount()
        + " | returns: " + m.getReturnType().getSimpleName()
    );
}

Constructors

// Instantiate with a specific constructor
Constructor<?> ctor = clazz.getDeclaredConstructor(String.class, int.class);
Object instance = ctor.newInstance("Alice", 30);

4. Invoking Methods Dynamically

Once you have a Method object, call invoke() with the target instance and arguments. This is the core of dynamic dispatch.

public class Calculator {
    public int add(int a, int b) { return a + b; }
}

// --- Reflection call ---
Calculator calc = new Calculator();
Method addMethod = Calculator.class.getMethod("add", int.class, int.class);

Object result = addMethod.invoke(calc, 3, 7);
System.out.println(result); // 10

💡 Tip: getMethod() searches public methods including inherited ones. Use getDeclaredMethod() to target a specific class's own methods regardless of visibility.

5. Accessing Private Members

Reflection can bypass Java's access control — a double-edged sword. You must call setAccessible(true) before reading or writing a private member.

public class Secret {
    private String password = "hunter2";
}

Secret obj = new Secret();
Field f = Secret.class.getDeclaredField("password");
f.setAccessible(true);

System.out.println(f.get(obj)); // hunter2
f.set(obj, "newpass");          // mutation!

⚠️ Warning: In Java 16+ (strong encapsulation via the Module System), setAccessible() on JDK internal classes may throw InaccessibleObjectException. Always handle this with a try-catch and prefer proper APIs when available.

6. Annotations via Reflection

Reflection is the standard way to read custom annotations at runtime. This is the backbone of frameworks that use annotations as metadata (e.g. @Autowired, @Entity, @Test).

@Retention(RetentionPolicy.RUNTIME)
@Target(ElementType.METHOD)
public @interface Log {
    String level() default "INFO";
}

public class Service {
    @Log(level = "DEBUG")
    public void processOrder() { /* ... */ }
}

// Reading annotation at runtime
Method m = Service.class.getMethod("processOrder");
if (m.isAnnotationPresent(Log.class)) {
    Log log = m.getAnnotation(Log.class);
    System.out.println("Log level: " + log.level()); // DEBUG
}

7. Real-World Use Cases

Use Case	Example	How Reflection Helps
Dependency Injection	Spring `@Autowired`	Scans beans and injects dependencies without manual wiring
ORM Mapping	Hibernate `@Entity`	Maps Java fields to DB columns using annotations
Serialization	Jackson ObjectMapper	Reads fields/getters to convert objects to JSON automatically
Unit Testing	JUnit `@Test`	Discovers and invokes test methods dynamically
Plugin Systems	Custom CLIs / IDEs	Loads and runs classes from external JARs at runtime

8. Performance & Pitfalls

Performance cost

Reflective calls are significantly slower than direct calls — typically 10–100x for cold invocations. The JIT compiler cannot inline or optimize them as effectively. Always cache Method, Field, and Constructor objects rather than looking them up on every call.

// ❌ Slow: lookup inside a hot loop
for (Object o : items) {
    Method m = o.getClass().getMethod("process"); // repeated lookup!
    m.invoke(o);
}

// ✅ Fast: cache the Method once
Method m = MyClass.class.getMethod("process");
for (Object o : items) {
    m.invoke(o); // cached — much cheaper
}

Key pitfalls to avoid

Breaking encapsulation — calling setAccessible(true) carelessly can violate the module system in Java 16+
Swallowed exceptions — invoke() wraps target exceptions in InvocationTargetException; always unwrap and handle them
Overusing reflection — prefer type-safe, compile-time alternatives when the type is known
AOT incompatibility — reflection can hinder GraalVM Native Image compilation; register reflective access explicitly if needed

💡 Modern alternative: For high-performance dynamic dispatch, consider MethodHandle (Java 7+) or VarHandle (Java 9+). They are faster than classic Reflection and integrate better with the JIT.

Wrapping Up

Java Reflection is an incredibly powerful tool — it powers the frameworks you use every day. The key is to use it intentionally: reach for it when you genuinely need runtime flexibility, cache your reflective objects, and always handle the checked exceptions it throws. When performance is critical, MethodHandle is your friend.

Now go build something meta. Happy coding! 🚀

If you found this helpful, drop a ❤️ and feel free to share!

How I Rebuilt a Three-Layer Cache System in Java — Redis, L1, and MongoDB Done Right

Mustafa Bingül — Sat, 25 Apr 2026 19:03:03 +0000

I've been working on Nexus, a backend infrastructure project, and recently hit a point where the data synchronization layer needed a serious rethink. What looked like a working cache system turned out to have a broken hierarchy, silent data loss paths, race conditions, and a latent deadlock waiting to happen.

This post walks through every problem I found in the original code and exactly how I fixed each one. Code comparisons included throughout.

The Architecture: What We're Building

The system manages data across three layers:

┌─────────────────────────────────┐
│        Redis Cache (MASTER)     │  ← single source of truth
└────────────┬──────────┬─────────┘
             │          │
       pull 10s      flush 15s
             │          │
┌────────────▼──┐  ┌────▼──────────────┐
│  L1 Cache     │  │     MongoDB        │
│  (in-memory)  │  │  (persistent DB)   │
└───────────────┘  └────────────────────┘

The rules are simple:

Redis is always master. No other layer can override it.
L1 is an in-memory mirror of Redis. It reads from Redis, never the other way around.
MongoDB is the persistent backup. Redis writes to it — not the other way around.

Three scheduled tasks keep everything in sync:

Task	Interval	Job
L1 Sync	10s	Pull Redis → update L1 if changed
Auto Flush	15s	Push dirty keys from Redis → MongoDB
Reconciliation	3 min	Compare Redis ↔ MongoDB, Redis wins

Now let's look at what was actually wrong with the original implementation.

Problem 1: The Hierarchy Was Backwards

This was the most fundamental issue. The reconciliation task was supposed to enforce Redis as master — but it was doing the opposite.

The original code

private void startReconciliationTask() {
    redisManager.processTask(() -> {
        idToDataList.forEach((keyTag, model) -> {
            if (dirtyKeys.contains(keyTag)) return;

            NexusApplication.getApplication().getMongoManager()
                .getValue(model.getAddon(), model.getSpecificDbKey())
                .thenAccept(dbJson -> {
                    if (dbJson == null) return;
                    try {
                        String cleanDbJson = model.getAddon().modelInitComp(dbJson);
                        if (!cleanDbJson.equals(model.getValueJson())) {
                            model.setValueJson(cleanDbJson);          // ← overwriting L1 with Mongo
                            redisManager.setData(keyTag, cleanDbJson); // ← overwriting Redis with Mongo!
                        }
                    } catch (Exception e) {
                        e.printStackTrace();
                    }
                });
        });
    });
}

When MongoDB had a different value than Redis, it was writing MongoDB's value into Redis. MongoDB was effectively acting as master. The entire priority chain was inverted.

The fix

private void startReconciliationTask() {
    RedisManager rm = NexusApplication.getApplication().getRedisManager();

    rm.processTask(() -> {
        // ...
        // Get the master value from Redis first
        String redisJson = rm.getData(key).orElseGet(model::getValueJson);

        NexusApplication.getApplication().getMongoManager()
            .getValue(model.getAddon(), model.getSpecificDbKey())
            .thenAccept(dbJson -> {
                // ...
                String cleanDbJson = model.getAddon().modelInitComp(dbJson);

                // Redis wins. If Mongo is different, update Mongo — not Redis.
                if (!cleanDbJson.equals(redisJson)) {
                    NexusApplication.getApplication().getMongoManager()
                        .setValue(model.getAddon(), model.getSpecificDbKey(), redisJson);
                    model.setValueJson(redisJson); // L1 follows Redis
                }
            });
    });
}

Redis is fetched first and treated as the ground truth. When there's a mismatch, MongoDB gets corrected — never Redis.

Problem 2: Silent Data Loss on Flush Failure

The auto flush task was removing the dirty flag before confirming the MongoDB write succeeded.

The original code

private void startAutoSyncTask() {
    List<String> keysToSync = new ArrayList<>(dirtyKeys);
    dirtyKeys.removeAll(keysToSync); // ← removed before writing to Mongo

    redisManager.processTask(() -> {
        for (String key : keysToSync) {
            DataModel model = idToDataList.get(key);
            if (model != null) {
                NexusApplication.getApplication().getMongoManager()
                    .setValue(model.getAddon(), model.getSpecificDbKey(), model.getValueJson());
                // if this fails, the key is already gone from dirtyKeys
                // it will never be retried
            }
        }
    });
}

If the MongoDB write threw an exception or the future completed exceptionally, the dirty flag was already removed. The entry would never be retried. Data was silently lost.

The fix

private void startAutoFlushTask() {
    List<String> keysToFlush = new ArrayList<>(dirtyKeys); // snapshot — don't removeAll yet

    rm.processTask(() -> {
        for (String key : keysToFlush) {
            DataModel model = keyToModel.get(key);
            if (model == null) {
                dirtyKeys.remove(key);
                continue;
            }

            String jsonToWrite = rm.getData(key).orElseGet(model::getValueJson);

            try {
                NexusApplication.getApplication().getMongoManager()
                    .setValue(model.getAddon(), model.getSpecificDbKey(), jsonToWrite)
                    .get(); // block until Mongo confirms the write

                dirtyKeys.remove(key); // only remove AFTER confirmed success

            } catch (Exception e) {
                // leave the key dirty — it will be retried on the next flush
                LOGGER.log(Level.SEVERE, "[AutoFlush] Write failed, will retry: " + key, e);
            }
        }
    });
}

Two changes here: the dirty flag snapshot is taken but not immediately cleared, and dirtyKeys.remove(key) only runs after .get() confirms the write succeeded. If anything goes wrong, the key stays dirty and gets retried 15 seconds later.

Problem 3: TOCTOU Race Condition in removeModel()

TOCTOU stands for Time-of-Check to Time-of-Use. The original removeModel() checked if a key existed before removing it — but another thread could delete it between those two operations.

The original code

public void removeModel(String key) {
    if (idToDataList.containsKey(key)) {  // Thread A checks: key exists
        // --- Thread B deletes the key here ---
        dirtyKeys.remove(key);
        idToDataList.remove(key);          // Thread A removes: but key is already gone
        NexusApplication.getApplication().getRedisManager().deleteData(key);
    }
}

In a concurrent environment, this is not safe. The containsKey check and remove call are two separate operations with no atomicity guarantee between them.

The fix

public void removeModel(String key) {
    DataModel removed = keyToModel.remove(key); // atomic: check + remove in one step
    if (removed == null) return; // wasn't there — nothing to do

    idToKey.remove(removed.getId());
    dirtyKeys.remove(key);
    NexusApplication.getApplication().getRedisManager().deleteData(key);
}

ConcurrentHashMap.remove() is atomic. Its return value tells you whether anything was actually removed. One operation, no race.

Problem 4: Deadlock Risk in Reconciliation

The original reconciliation task was dispatching a new processTask from inside an already-running processTask.

The original code

redisManager.processTask(() -> {
    idToDataList.forEach((keyTag, model) -> {
        // ...
        NexusApplication.getApplication().getMongoManager().getValue(...)
            .thenAccept(dbJson -> {
                // ...
                redisManager.processTask(() ->   // ← new task dispatched from inside a running task
                    NexusApplication.getApplication().getMongoManager()
                        .setValue(...));
            });
    });
});

If processTask uses a single-threaded executor (which is common for Redis clients to ensure command ordering), submitting a new task from inside a running task means the inner task can never start — the outer task is blocking the only available thread. That's a deadlock.

The fix

Everything runs in a single processTask context. The MongoDB writes inside thenAccept are plain async calls — no nested processTask.

rm.processTask(() -> {
    for (String key : keys) {
        // ...
        CompletableFuture<?> future = NexusApplication.getApplication()
            .getMongoManager()
            .getValue(...)
            .thenAccept(dbJson -> {
                // ...
                // direct Mongo write — no nested processTask
                NexusApplication.getApplication().getMongoManager()
                    .setValue(...);
            });

        batch.add(future);
        if (batch.size() >= RECONCILE_BATCH_SIZE) {
            waitForBatch(batch);
            batch.clear();
        }
    }
});

All reconciliation work happens inside one task. No task dispatches another task.

Problem 5: MongoDB Request Storm on Large Datasets

The original reconciliation fired a MongoDB query for every single entry simultaneously — no throttling, no batching.

On 1000+ entries, that means 1000+ concurrent MongoDB reads, followed immediately by potentially 1000+ writes. This can exhaust connection pools, spike latency, and cause cascading failures under load.

The fix: batch processing

private static final int RECONCILE_BATCH_SIZE = 50;

// inside startReconciliationTask:
List<CompletableFuture<?>> batch = new ArrayList<>(RECONCILE_BATCH_SIZE);

for (String key : keys) {
    if (dirtyKeys.contains(key)) continue;
    // ...

    CompletableFuture<?> future = mongoManager.getValue(...).thenAccept(...);
    batch.add(future);

    if (batch.size() >= RECONCILE_BATCH_SIZE) {
        waitForBatch(batch); // wait for all 50 to complete
        batch.clear();       // then start the next 50
    }
}

if (!batch.isEmpty()) waitForBatch(batch);

private void waitForBatch(List<CompletableFuture<?>> batch) {
    try {
        CompletableFuture.allOf(batch.toArray(new CompletableFuture[0])).get();
    } catch (Exception e) {
        LOGGER.log(Level.WARNING, "[Reconciliation] Batch wait error", e);
    }
}

At most 50 concurrent MongoDB requests are in flight at any time. The next batch only starts when the current one is done. This is easy to tune — bump RECONCILE_BATCH_SIZE if your MongoDB can handle more concurrency, lower it under constrained environments.

Problem 6: Redis Evict/TTL Was Silently Ignored

Redis can evict keys under memory pressure or when a TTL expires. The original L1 Sync did nothing when it detected a missing key.

The original code

redisManager.getData(key).ifPresent(redisJson -> {
    if (!redisJson.equals(model.getValueJson())) {
        model.setValueJson(redisJson);
    }
    // if getData() returns empty — we just skip it silently
    // L1 is now out of sync with an evicted Redis key
});

When a key was evicted from Redis, L1 kept its stale value indefinitely. The next flush would try to read from Redis, get nothing, fall back to L1's stale data, and write that to MongoDB — potentially losing the newer value that was in Redis before eviction.

The fix

Optional<String> redisOpt = rm.getData(key);

if (redisOpt.isPresent()) {
    String redisJson = redisOpt.get();
    if (!redisJson.equals(model.getValueJson())) {
        model.setValueJson(redisJson); // L1 follows Redis
    }
} else {
    // Key was evicted from Redis — restore it from L1 and re-dirty
    LOGGER.warning("[L1Sync] Redis key missing, restoring: " + key);
    rm.setData(key, model.getValueJson()); // restore Redis from L1
    dirtyKeys.add(key);                    // trigger a Mongo re-write too
}

When Redis doesn't have a key, L1's value is pushed back into Redis (restoring the master) and the key is marked dirty so the flush task re-persists it to MongoDB.

Problem 7: addModelFix() Left Redis Empty

This method was supposed to handle externally-provided data, but it only wrote to L1 — leaving Redis without the key.

The original code

public void addModelFix(String key, DataModel model) {
    idToDataList.put(key, model); // writes to L1 only
    dirtyKeys.add(key);
    // Redis has no entry for this key
    // Next L1 Sync will detect the missing Redis key, restore it from L1,
    // and re-dirty it — unnecessary extra cycle
}

The fix

public void addModelFix(String key, DataModel model) {
    writeToL1AndRedis(key, model); // writes both L1 and Redis together
    dirtyKeys.add(key);
}

All write paths now go through the same internal method, which guarantees both layers are always updated together.

Problem 8: O(n) ID Lookup

The getDataModelFromId() method streamed through the entire map on every call.

The original code

public Optional<DataModel> getDataModelFromId(String id) {
    return idToDataList.values().stream()
        .filter(dm -> dm.getId().equals(id))
        .findAny(); // O(n) — scans the whole map
}

On 1000 entries this is 1000 comparisons per lookup. If this method is called frequently (e.g., per player action in a game server), it compounds fast.

The fix: reverse index

A second ConcurrentHashMap keeps a id → key mapping updated alongside the main map.

// new field
private final ConcurrentHashMap<String, String> idToKey;

// updated on every write
private void writeToL1AndRedis(String key, DataModel model) {
    keyToModel.put(key, model);
    idToKey.put(model.getId(), key); // maintain reverse index
    NexusApplication.getApplication().getRedisManager().setData(key, model.getValueJson());
}

// and on remove
public void removeModel(String key) {
    DataModel removed = keyToModel.remove(key);
    if (removed == null) return;
    idToKey.remove(removed.getId()); // keep reverse index clean
    // ...
}

// lookup is now O(1)
public Optional<DataModel> getDataModelFromId(String id) {
    String key = idToKey.get(id);
    if (key == null) return Optional.empty();
    return Optional.ofNullable(keyToModel.get(key));
}

Two map lookups instead of a full scan. The memory cost is minimal — just a second map of strings.

The Final Picture

Here's a summary of every change made:

#	Problem	Impact	Fix
1	Hierarchy inverted — Mongo was overriding Redis	Wrong data served	Redis fetched first; Mongo updated to match Redis
2	Dirty flag removed before Mongo write confirmed	Silent data loss	`dirtyKeys.remove()` called only after `.get()` succeeds
3	TOCTOU race in `removeModel()`	Potential NPE / double-delete	Single atomic `remove()` with return value check
4	Nested `processTask` in reconciliation	Deadlock on single-thread executor	All work in one task context, no inner dispatch
5	All entries queried simultaneously in reconciliation	MongoDB connection storm	Batch processing (50 at a time) with `CompletableFuture.allOf()`
6	Redis evict/TTL silently ignored in L1 Sync	Stale L1 data, wrong Mongo writes	Restore Redis from L1, mark dirty
7	`addModelFix()` skipped Redis	Redis missing key, extra sync cycle	Unified `writeToL1AndRedis()` for all write paths
8	`getDataModelFromId()` was O(n)	CPU pressure on 1000+ entries	`id → key` reverse index for O(1) lookup

Takeaway

The original code wasn't obviously broken — it ran, it synced, it mostly worked. The bugs were in the edge cases: what happens when Mongo is unavailable for one cycle, when Redis evicts a key under pressure, when two threads hit removeModel() at the same time.

Distributed cache management is one of those areas where the details really matter. A clear layer hierarchy and explicit failure guarantees aren't optional extras — they're what separate a system that works from one that almost always works.

The full source is available in the v1.1.0 release.

If you spotted something I missed or have a different approach to any of these problems, I'd love to hear it in the comments.

I got tired of heavyweight frameworks, so I built my own web server in Java

Mustafa Bingül — Fri, 24 Apr 2026 20:30:58 +0000

I got tired of heavyweight frameworks, so I built my own web server in Java

Most Java web projects start with Spring Boot. You add the dependency, and suddenly you have an entire ecosystem loaded into memory before your first route is even registered. For a lot of use cases that's totally fine. But I wanted something I could drop a JAR into a folder, double-click, and have a running web server in under three seconds — no config ceremony, no classpath drama.

So I built Aurelius.

What is it?

Aurelius is a lightweight, minimalist Java web server designed for developers who want to get a site or a small API running without pulling in a full framework. It runs on Java 8+, ships as a single executable JAR (or .exe on Windows), and is built on top of Netty for the networking layer.

The core idea: drop the JAR in a folder, launch it, put your HTML files in the right place, and you have a running server. That's it.

Project structure

When Aurelius starts, it expects this layout:

my-project/
├── aurelius.jar
├── settings.yml
├── app/
│   └── main.html        ← served at /
│   └── about/
│       └── main.html    ← served at /about
├── containers/
│   └── button.html
│   └── section.html
└── public/
    └── logo.png
    └── data.json

Every folder under app/ becomes a route. main.html in a folder is the index for that route. Files under public/ are served statically and accessible from any HTML file via /public/filename.

The container system

This is the part I'm most proud of. Instead of reaching for a templating engine, I built a simple component system directly into the server.

You define a reusable HTML fragment in the containers/ folder:

<!-- containers/button.html -->
<button class="btn btn-primary">{text}</button>

Then you use it anywhere in your pages with a custom tag:

<container.button text="Click me"></container.button>

Aurelius processes the tag at serve time, injects the attribute values into the fragment, and sends the assembled HTML to the client. No JavaScript required, no build step, no npm.

You can nest containers, pass multiple attributes, and compose entire page sections from reusable fragments. It's not React — it's intentionally much simpler — but for static-ish sites it removes a lot of repetition.

Placeholder system

For site-wide variables, Aurelius has a placeholders.yml file:

title: "My Site"
author: "Mustafa"
version: "1.2"

Anywhere in your HTML you write %title% and Aurelius substitutes it at serve time:

<head>
  <title>%title% — Home</title>
</head>

This is useful for things like site name, meta descriptions, or any string you'd otherwise duplicate across dozens of pages.

RESTful API support via addons

Aurelius isn't just for serving HTML. You can register REST endpoints programmatically through the addon system:

// GET /api/hello
AddonManager.registerRestFulService(
    new RestFulResponseStructure.Builder("hello")
        .setRestFulResponse((body, helper) -> {
            return "Hello, world! Path: " + Arrays.toString(helper.getPathData());
        })
        .setRequestType(RestFulRequestType.GET)
        .build()
);

For structured request/response bodies, you implement the RestFulResponse<Output, Input> interface and Aurelius handles JSON deserialization automatically:

public class UserController implements RestFulResponse<UserDto, UserRequest> {

    @Override
    public UserDto response(UserRequest request, RestFulResponseHelper helper) {
        UserDto dto = new UserDto();
        dto.setUsername(request.getUsername());
        return dto;
    }

    @Override
    public UserRequest convert(String body) throws Exception {
        return (UserRequest) AddonManager.convertFromBodyJson(body, UserRequest.class);
    }
}

Cookies are also supported through the helper:

CookieStructure cookie = new CookieStructure();
cookie.setCookieName("session");
cookie.setCookieValue(UUID.randomUUID().toString());
cookie.setFeatures(Arrays.asList(new CFMaxAge(3600), new CFHttpOnly()));
helper.sendCookie(cookie);

Configuration

All server settings live in settings.yml:

server:
  port: 8080
  threadSize: 4
  ui: true

ui: true enables a built-in management panel where you can start, stop, and reload the server without touching the terminal. Useful if you're deploying somewhere without a great CLI experience.

Framework support

Because Aurelius just serves HTML, it works with any CSS framework that can be loaded from a CDN. Tailwind, Bootstrap, Bulma — just drop the <link> tag in your HTML and it works.

Who is this for?

Aurelius is a good fit if you:

Want a zero-dependency web server for a small internal tool or personal site
Are learning Java web development and want to see how a server works without framework magic hiding everything
Need to prototype a REST API quickly without standing up a Spring application
Want to understand Netty's HTTP handling without writing the boilerplate yourself

It's not the right tool for large production applications with complex routing, authentication middleware, or ORM integration. For those, reach for Spring Boot or Quarkus.

What's next

The main thing on my list is proper test coverage — the container parsing and placeholder substitution logic especially need unit tests. I'm also thinking about WebSocket support and a simple session management API.

11 releases in, Aurelius handles the basics solidly. If you're the kind of developer who likes understanding what's running under the hood, give it a try.

I built a centralized data orchestration engine for distributed Minecraft servers — here's how

Mustafa Bingül — Fri, 24 Apr 2026 20:28:44 +0000

I built a centralized data orchestration engine for distributed Minecraft servers

This post is about a real architectural problem I ran into while working on a multi-server Minecraft network — and how I solved it by building a dedicated data microservice called Nexus Core.

The problem

A large Minecraft network runs many game servers at the same time: lobby servers, PvP arenas, survival worlds, minigame instances. Each one is a separate JVM process running a Spigot plugin. The traditional approach is simple — every server connects directly to MongoDB, manages its own in-memory cache, and handles its own data logic.

This works fine at small scale. At larger scale, three problems emerge:

Connection pool exhaustion. Each server holds its own pool of MongoDB connections. With 20 servers, you're throwing 20× the connections at your database for no good reason.
Cache incoherency. Server A caches a player's balance. Server B modifies it. Server A's cache is now stale. The player sees incorrect data depending on which server they're on.
Duplicated logic. The same data access code exists in every plugin. Changing how player data is structured means touching 15 codebases.

The solution: one service that owns all data

Nexus Core is a standalone Java application that acts as the single source of truth for all data in the network. Game servers never query MongoDB directly. Instead, they publish a structured packet to a Redis channel, and Nexus Core handles the rest.

Spigot #1 ─┐
Spigot #2 ──► Redis (pub/sub) ──► Nexus Core ──► MongoDB
Spigot #3 ─┘

This gives us:

A single, optimized MongoDB connection pool
A centralized Redis cache that all servers read from
Data logic defined once per data type, shared everywhere

The packet protocol

Every request from a game server is a JSON packet published to a Redis channel:

{
  "protocol": 100,
  "source": "pvp-1",
  "type": "GET_DATA",
  "data": { "uuid": "550e8400-e29b-41d4-a716-446655440000" }
}

protocol is a numeric ID that identifies which data type is being requested. source is the server's identifier, used to publish the response back to the right channel. type is one of GET, SET, DELETE, or GET_ALL.

Nexus Core subscribes to the inbound channel, deserializes the packet, and routes it to the right handler in O(1) via a Map<Integer, DataAddon>.

The DataAddon abstraction

A DataAddon is an abstract class that you extend to define a new data type. It maps to exactly one MongoDB collection and one Redis cache namespace. Here's what a minimal addon looks like:

public class PlayerStatsAddon extends DataAddon {

    @Override public int addonId()          { return 100; }
    @Override public String addonName()     { return "Player Stats"; }
    @Override public String getDatabase()   { return "nexus_core_db"; }
    @Override public String getCollection() { return "player_stats"; }
    @Override public String cacheKeyHeaderTag() { return "stats"; }

    @DbDataModels(isId = true)
    private String uuid;

    @DbDataModels(defaultValue = "0", isId = false)
    private int kills;

    @DbDataModels(defaultValue = "0", isId = false)
    private int deaths;

    @Override
    public boolean handleRequest(String source, RequestType type, NexusJsonDataContainer data) {
        return true; // return false to reject the request
    }
}

Nexus Core discovers the schema at boot time via reflection over @DbDataModels annotations. It finds which field is the primary key, reads default values for missing fields, and knows how to serialize/deserialize documents without you writing any mapping code.

Registering the addon is one line:

NexusApplication.getInstance().getProtocolHandler().registerAddon(new PlayerStatsAddon());

From that point on, any game server can send a packet with protocol: 100 and get a full player stats document back.

Request lifecycle

Here's the full flow for a GET request:

Game server publishes packet to Redis
Nexus Core receives it and looks up the addon by protocol ID
handleRequest() is called — if it returns false, an empty response is sent back immediately
Nexus Core checks Redis for key stats:{uuid}
Cache hit → serialize and publish response
Cache miss → query MongoDB, write result to Redis, publish response

SET writes through to both MongoDB and Redis. DELETE removes from both. GET_ALL bypasses cache entirely — caching arbitrary result sets would make invalidation logic very complex.

The handleRequest() interceptor

This is one of my favorite parts of the design. Before any database operation happens, your addon's handleRequest() method is called synchronously. You can inspect the source server, the request type, and the payload — and reject the request outright by returning false.

This is where authorization lives. For example:

@Override
public boolean handleRequest(String source, RequestType type, NexusJsonDataContainer data) {
    if (type == RequestType.REMOVE_DATA) {
        return source.equals("admin"); // only admin server can delete
    }
    return true;
}

Keep this method fast — it runs synchronously and blocks the request pipeline.

Monitoring dashboard

Nexus Core ships with a live monitoring UI built entirely in Java Swing with Java2D — no external libraries. It shows real-time JVM CPU load, process RAM usage, number of objects currently in the Redis cache, and the count of active addons. All rendered as animated donut charts and scrolling line graphs.

What I'd do differently

Looking back, the main thing missing is test coverage. The addon system and cache invalidation logic are exactly the kind of thing that benefits from solid unit tests, and I didn't write them. That's the next thing I'm adding.

I'd also consider replacing the protocol integer with a string identifier — numeric IDs work fine but require a separate constants file to stay readable, which is an avoidable friction point.

Repo

https://github.com/mustafabinguldev/nexus-core

If you're building anything on distributed Java infrastructure or have thoughts on the addon model, I'd love to hear from you in the comments.

DEV Community: Mustafa Bingül

Why InfluxDB Is the Go-To Database for Time-Series Data (With Real Examples)

Why InfluxDB Is the Go-To Database for Time-Series Data 🚀

🤔 What Is InfluxDB?

⚡ Why InfluxDB? The Core Advantages

1. Purpose-Built for Time-Series

2. Automatic Data Compression

3. Retention Policies (Built-In TTL)

4. Flux — A Powerful Query Language

5. Native Integrations

🧠 Core Concepts

🛠️ Getting Started

Run InfluxDB Locally with Docker

🐍 Python Examples

Install the Client

Writing Data

Writing Batch Data (Simulated Sensor Readings)

Querying Data with Flux

📊 Flux Query Language — Quick Reference

Filter by Tag

Aggregate Over Time Windows

Detect Anomalies with movingAverage

Join Two Measurements

🌍 Real-World Use Cases

1. Infrastructure Monitoring

2. IoT Sensor Data

3. Application Performance Monitoring (APM)

4. Financial Market Data

⚠️ When NOT to Use InfluxDB

🆚 InfluxDB vs. Alternatives

🏁 Summary

📚 Further Reading

I Rebuilt the Core of My Data Engine — Here's What Changed in v1.3.0

I Rebuilt the Core of My Data Engine — Here's What Changed in v1.3.0

What is Nexus Core?

🫀 Live Protocol — Heartbeat Every Second

🗄️ Cache — Dynamic TTL per DataAddon

🔄 L1 / L2 Auto-Invalidation via Keyspace Notifications

⚡ Dual-Queue Worker Architecture

🔍 Reflection Caching

Architecture Summary

Java Reflection API: Inspect & Manipulate Code at Runtime

Java Reflection API: Inspect & Manipulate Code at Runtime

Table of Contents

1. What is Reflection?

2. Getting a Class Object

3. Inspecting Fields, Methods & Constructors

Fields

Methods

Constructors

4. Invoking Methods Dynamically

5. Accessing Private Members

6. Annotations via Reflection

7. Real-World Use Cases

8. Performance & Pitfalls

Performance cost

Key pitfalls to avoid

Wrapping Up

How I Rebuilt a Three-Layer Cache System in Java — Redis, L1, and MongoDB Done Right

The Architecture: What We're Building

Problem 1: The Hierarchy Was Backwards

The original code

The fix

Problem 2: Silent Data Loss on Flush Failure

The original code

The fix

Problem 3: TOCTOU Race Condition in removeModel()

The original code

The fix

Problem 4: Deadlock Risk in Reconciliation

The original code

The fix

Problem 5: MongoDB Request Storm on Large Datasets

The fix: batch processing

Problem 6: Redis Evict/TTL Was Silently Ignored

The original code

The fix

Problem 7: addModelFix() Left Redis Empty

The original code

The fix

Detect Anomalies with `movingAverage`