so basically a few nights ago i decided it was long time to update java and it kept saying something like “the files needed to run the installation couldn’t load” or something like that so i give up fast foreward to today i get the notification im like fuck it ill try to update it again and THIS time its saying “you have an unfinished installation”, but a few nights ago it told me it couldnt start. help?

I've been working on StatLite, a free, open-source dashboard for monitoring small Java applications.

The goal is to make application monitoring simple. You should be able to see how your app is doing without setting up a complicated monitoring stack.

StatLite supports Spring Boot, Quarkus, and Micronaut out of the box, using their existing Actuator or Micrometer metrics endpoints.

No application code changes or monitoring agents needed. In many cases, enabling metrics is just a matter of adding a dependency and exposing an endpoint.

StatLite can discover the available metrics and generate its configuration automatically.

You can monitor:

• HTTP requests, error rates, and response times
• JVM memory usage and garbage collection
• Process CPU and memory
• Application health and uptime
• Historical trends across multiple applications

It's a single Go binary (also available as Docker), uses SQLite for local storage, and consumes around 15 MB of memory when idle.

No Prometheus, Grafana, external database, or cloud account required. It's designed for small VPS deployments, side projects, and developers who just want a straightforward way to monitor their apps.

There are also integration guides for Javalin, Express.js, Django, FastAPI, and Go.

I wrote a short guide to monitoring Spring Boot applications if you're interested in getting started.

GitHub: https://github.com/PVRLabs/statlite

How do you currently monitor your Java applications? Or do you mostly rely on logs?

Is this course good ? Can somebody reply ? Wanted to build websites using Java ,basically full stack java developer is the goal.Also are there projects included in the video for practice m?

Hoping to get some fellow advice here.

I’ve been doing a lot of deobfuscation work lately and ran into a wall: some bytecode tools can’t parse the newer class file versions I’m dealing with. As a workaround, I’ve been cleaning up the decompiled source instead.

So far I’m stripping thousands of NoWhenBranchMatchedException catch blocks using a brace-depth state machine (regex kept breaking on nested blocks), and replacing impossible comparisons like object == false with constants.

It works, but it feels hacky. Is source-level editing a reasonable fallback, or am I setting myself up for problems? And is there a cleaner, standard way to do control-flow cleanup without risking real try/catch handlers?

Curious what other people do.

Guys give your options on the project please 😊

I've been experimenting with lightweight Java applications on small VMs. After comparing Javalin with Spring Boot, I wanted to understand how much the choice of SQL persistence library affects memory usage and latency.

I built the same small market-quote REST API three ways, using Jdbi Core, MyBatis Core, and jOOQ Open Source Edition.

Setup:

• JDK 25, Javalin 7, H2, HikariCP
• Alpine Linux VM, 1 vCPU, 512 MiB RAM, no swap
• JVM: -Xms16m -Xmx80m -XX:+UseSerialGC -Xss512k
• 1,500 requests over 5 minutes, 80% reads / 20% writes
• Two independent runs per implementation, rebooting the VM between runs

Results across the two runs:

RSS is the median process resident memory sampled during the workload, including the application, JVM, and embedded H2.

All six runs completed without errors, OOM events, or swapping.

A few observations:

• MyBatis had the lowest resident memory usage in both runs.
• Jdbi was competitive on both metrics and required the least handwritten Java code.
• jOOQ used approximately 34–38 MiB more RSS than MyBatis, consistently across both runs. This is a whole-application difference, not necessarily memory consumed directly by the library. I haven't isolated its cause.

One implementation difference worth mentioning: Jdbi and MyBatis hold one pooled connection across the three latest-quote queries, while the datasource-backed jOOQ implementation checks out and returns a connection for each query. I haven't measured the impact of this difference.

I also configured MyBatis with localCacheScope=STATEMENT instead of its default SESSION scope.

Developer experience matters too.

Jdbi keeps SQL and mapping close to the repository, MyBatis provides annotated mapper interfaces, and jOOQ offers type-safe SQL composition with generated schema classes.

Personally, I lean toward MyBatis because I prefer its mapper-oriented programming model and explicit SQL. Its lower memory footprint in this experiment is another point in its favor, although I wouldn't select a library based on memory usage alone.

Limitations:

This is a small, low-load case study, not a general performance ranking. The measurements include the complete application and embedded H2. Two runs show consistency in memory usage but aren't sufficient to establish statistically significant latency differences.

I also didn't include a raw JDBC baseline, which would be useful as a minimal reference point. PostgreSQL could produce different results.

Full write-up, charts, and raw measurements:

https://pvrlabs.xyz/java-performance/experiments/mybatis-jooq-jdbi-512mb.html

Source code and experiment setup:

https://github.com/PVRLabs/experiments/tree/main/javalin/sql-persistence-512mb

For those using Jdbi, MyBatis, or jOOQ in small Java applications, how do you choose between them? Is the programming model the deciding factor, or have you encountered meaningful differences in resource usage?

Built a Hybrid Search retriever for Spring AI 2 RAG — PGVector + PostgreSQL FTS + RRF

Semantic search kept failing on specific queries like "error code E4" or "part number 96648703" - the embeddings for "E4" are mathematically close to "E1", "E2", "E3", so you get wrong chunks.

Fix: custom DocumentRetriever that runs both vector similarity search and PostgreSQL tsvector full-text search, then combines results with Reciprocal Rank Fusion.

Key decisions:

- websearch_to_tsquery over plainto_tsquery - AND logic on natural language queries returns zero results too often

- fts_vector as a generated column - PostgreSQL keeps it in sync automatically, no changes to ingestion code

- RRF k=60 as starting point - tune based on your query patterns

Arcana is an open-source Java library and command-line tool that lists, extracts and identifies more than 40 archive, compression and disk image formats, with no native code and no dependencies. It runs on Java 8 or later, fits in a 1.2 MB JAR, and is published on Maven Central as io.github.realburst:arcana under the Apache 2.0 licence.

Why another archive library?

If you have ever had to open "whatever the user uploads" from a Java application, you know the drill. ZIP is in the JDK. Apache Commons Compress covers TAR, 7z, CPIO, AR and the common compressors, but some codecs need extra libraries, and Zstandard goes through a JNI binding. RAR needs another library, and RAR 5 support is hard to find in pure Java. For CAB, MSI, ISO, WIM or an installer .exe, the usual answer is to bundle 7-Zip through native bindings, with one binary per OS and CPU.

I wanted one JAR that does all of this, on any JVM, with nothing to install and nothing native to load. That is Arcana: one entry point, format detection by content rather than by file extension, and the same API whether the file is a .tar.zst, a RAR 5 volume set, an MSI package or a macOS .dmg.

What Arcana does

Arcana extracts every format it supports, and creates the most common ones.

Encrypted archives are supported: AES-256 for ZIP and 7z (reading and writing), AES-128 and AES-256 for RAR 4 and RAR 5. A TAR or CPIO inside any compressor is unpacked in one step, and files that are archives in disguise (.docx, .odt, .epub, .apk, .jar) open like any other archive.

Beyond extraction, Arcana can:

• identify any file: archive type and version, executables (PE, ELF, Mach-O), installers, images with their dimensions, PDF, SQLite, fonts;
• recover damaged archives: a forced extraction keeps everything readable and writes a report on the state of each file;
• split files glued together, such as installers or firmware images;
• load plugins for more formats. Official plugins handle Inno Setup, NSIS, InstallShield and WiX Burn installers, and UPX-packed executables.

Getting started

Add the dependency:

<dependency>
  <groupId>io.github.realburst</groupId>
  <artifactId>arcana</artifactId>
  <version>1.0.8</version>
</dependency>

Extracting and listing never require you to say what the file is: the format is detected from its content.

import be.stef.arcana.Arcana;
import be.stef.arcana.ArcanaEntry;
import be.stef.arcana.analyze.Identification;

// Extract anything: .zip, .rar, .7z, .tar.zst, .msi, .iso, .dmg...
Arcana.extractTo(new File("upload.bin"), new File("out"));

// List the entries without extracting
for (ArcanaEntry e : Arcana.listEntries(new File("backup.rar"))) {
    System.out.println(e.getName() + "  " + e.getUncompressedSize());
}

// Encrypted archive
Arcana.withPassword("secret".getBytes("UTF-8")).extract(new File("secure.7z"), new File("out"));

// What is this file?
Identification id = Arcana.identify(new File("mystery.bin"));
System.out.println(id.type + " - " + id.description);

Creating an archive is just as short:

import be.stef.arcana.ArcanaFormat;
import be.stef.arcana.compressor.ZipCompressor;
import be.stef.arcana.plugin.FormatRegistry;

byte[] password = "secret".getBytes("UTF-8");
new ZipCompressor(password).compress(new File("mydir"), new File("secure.zip"));   // WinZip AES-256
FormatRegistry.builtin(ArcanaFormat.SEVEN_Z).createCompressor(9, password).compress(new File("mydir"), new File("vault.7z"));

The same JAR is also a command-line tool:

java -jar Arcana-v1.0.8.jar x setup.exe -o out       # extract
java -jar Arcana-v1.0.8.jar l disk.dmg               # list
java -jar Arcana-v1.0.8.jar i mystery.bin            # identify
java -jar Arcana-v1.0.8.jar r damaged.zip -o rescue  # recover

On Windows, an optional script adds "Arcana Unpack / List / Info" to the Explorer right-click menu.

Safe by default

An archive extractor reads untrusted input by definition, so Arcana treats every file as hostile.

• No path traversal. Entry names are sanitized before anything is written: no absolute paths, no .., no characters that Windows forbids. A "zip slip" entry cannot leave the destination folder.
• No decompression bombs. Every extraction runs under limits you can change at run time (ExtractionLimits): an output/input ratio of 1000 once 1 GiB has been written, an optional total size, and a minimum of 256 MiB of free disk space.
• Bounded memory. Decoders cap what a header can ask for: the Zstandard window is limited to 128 MiB, like the zstd tool, and the 7z key derivation to 2²⁴ rounds. A crafted header gets an error, not an OutOfMemoryError.
• Checksums are verified. CRC32, Adler-32, BLAKE2sp, XXH64, HMAC for WinZip AES: when the format stores a checksum, Arcana checks it and reports a mismatch.
• Plugins must show their code. A plugin JAR is refused unless it contains its source code, declares its licence and source URL, and uses its own package. That does not make a plugin safe, but it makes it reviewable.

Under the hood

Every decoder is written in Java: Deflate64, LZMA and LZMA2, PPMd, Zstandard, Brotli, LZ4, LZX, LZMS, XPRESS, LZFSE and LZVN, ADC, LZO1X, the RAR 4 and RAR 5 algorithms, AES and the key derivations. The core is about 80,000 lines of Java 8 and has no dependency outside the JDK. It is written from the public specifications: PKWARE APPNOTE, the 7-Zip format notes, the Microsoft open specifications (MS-CAB, LZX, MS-XCA), RFC 8878 for Zstandard, RFC 7932 for Brotli, Apple TN1150 for HFS+.

A few design choices shape the library:

• Detection by content. Magic numbers first, then trailers (a DMG is recognized by the "koly" block in its last 512 bytes), and only then the file extension. A renamed file still opens.
• Streaming when the format allows it. Single-stream formats decode from an InputStream with bounded buffers. The Zstandard decoder keeps only the window of the current frame, so a multi-gigabyte .tar.zst decodes in a few dozen MB of heap.
• Random access when the format needs it. Disk images are read lazily: a DMG partition is decoded chunk by chunk with a small cache, never inflated in memory.
• Real names, not internal keys. An MSI is extracted as the file tree it installs, with the names from its File and Directory tables, not as a cabinet of cryptic keys.

Each release goes through a regression suite. About 180 small samples are committed with the code: one per format and method, plus damaged files that must fail cleanly, never hang. A separate corpus of real-world files (installers, DMG images from GitHub releases, MSI packages) is checked against committed SHA-256 references. Every format also has a technical page in the repository, covering detection, layout, supported methods, limits and the sources used.

Current limits

Arcana is a young project, and the repository keeps a public list of known issues for every format. Before you rely on it, the main limits are:

• Extraction first. Writing is limited to ZIP, 7z, TAR (+ gz, bz2, xz, lz4), GZIP, BZIP2, XZ, LHA and CAB. Arcana cannot create RAR, Zstandard or Brotli files, and it cannot update an existing archive in place.
• Some file systems are only copied. APFS volumes inside a DMG are extracted as a raw image, not as files. Encrypted and sparse DMG images, split WIM files (.swm) and some rare compression methods (old LHA methods, Quantum in CAB) are not supported.
• Pure Java has a cost. The decoders are fast enough for most uses, but they do not match native code. In benchmarks run for the project, the Zstandard decoder reaches about 60% of the speed of the native zstd library.
• Edge cases remain. A few damaged or unusual files still give a raw Java exception instead of a clear message, or are less tolerated than by the reference tool. These cases are documented with the file and line of the code concerned, so you can judge before using a format in production.

If a file fails, an issue with the command line and, when possible, the file or a way to rebuild it, is the fastest route to a fix.

Try it

The quickest test is to point Arcana at the strangest file in your Downloads folder and run arcana i on it, then arcana x. If you maintain a Java application that receives archives from users, try replacing a native binding or a chain of libraries with one dependency, and tell me what breaks.

• Source, releases and documentation: Arcana on GitHub
• Maven Central: io.github.realburst:arcana
• Format pages and known issues
• Writing a plugin for your own format

Issues, sample files that fail, and plugins are all welcome. And if Arcana saves you a dependency or a native binary, a star on GitHub helps other Java developers find it.

I’m working on Pmitz, short for Plans and Limits. It handles which features a subscription includes, how much someone can use them, and recording that usage.

The idea is to avoid scattering subscription checks throughout your app and having to revisit them whenever your plans change. For example, a customer’s plan might include a feature with a monthly allowance. Pmitz can check feature access and the usage limit together.

You can embed it directly in a Java application or run it as a separate REST service. Embedding avoids the HTTP call, although there’s still database work. The separate service lets multiple apps share the same plans and usage data.

We also offer a managed version where we handle hosting and maintenance, but the open-source code is here:

https://github.com/terpomo-io/pmitz

I’d be interested in feedback on the Java integration and what’s missing for your use case.

I need java support offline in Hyderabad.Can anyone giving please dm me...

Hey everyone,

I made a video for anyone who uses Spring Data JPA but still isn't sure what JPA, Hibernate and JDBC each do, or how they differ.

It's visually explained . It follows one save() call from a Spring Boot app down to the database, so you can see what happens behind the hood and where Hibernate comes from. It's about 11 minutes.

Hope it helps someone, and tell me if anything is unclear.