Engula

Engula
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Engula ValueSight

Overview of Engula

Engula is a high-performance caching kernel product, purpose-built for open-source Redis users seeking greater efficiency and lower operational costs. Its primary objective is to reduce hardware expenditure for existing Redis clusters while maintaining maximum compatibility and minimal risk.

Key Strengths

  • Minimal Core Modifications Engula maintains system stability and interoperability through lightweight kernel adjustments. The current release is 100% protocol-compatible with Redis 7.2.4.

  • Substantial Memory Efficiency Significantly reduces metadata overhead via a self-developed Compressed HybridLog Storage Engine, minimizing in-memory data representation.

  • Enhanced Performance Optimizations End-to-end performance improvements through:

    • Optimized HashTable structure
    • Efficient network thread communication
    • Improved CPU prefetch hit rates
    • Integration of an asynchronous thread framework

Based on early user adoption tests, Engula demonstrates over 50% average memory savings with a performance cost of less than 10%.

Engula ValueSight Overview

Engula ValueSight is a diagnostic and evaluation tool that enables users to quantitatively assess the benefits of adopting Engula in real-world scenarios. It allows one-click comparison of memory usage between Engula and Redis using actual production data, producing intuitive insight into Engula’s efficiency.

Core Capabilities

  • Memory Usage Analysis — Compare Resident Set Size (RSS) memory between Engula and Redis for identical datasets.
  • Visual Reporting — Generate detailed visual charts showing comparative memory profiles.
  • Comprehensive Summary Output — Produce concise analytical summaries illustrating key optimization insights.

Usage Instructions

Runtime Environment

Engula ValueSight is packaged as a Docker image, compatible with Linux and macOS environments that have Docker installed.

Recommended Environment:

Component Requirement
CentOS ≥ 7.9
Ubuntu ≥ 18.04
Docker Official version recommended (refer to the Docker Documentation)
CPU ≥ 2 physical cores

Running the Analysis

  1. Prepare an RDB File Generate a Redis dump.rdb using the BGSAVE command.

  2. Download the Engula ValueSight Image

    1docker pull registry.cn-guangzhou.aliyuncs.com/montplex/engula-valuesight

  3. Create an Output Directory

    1mkdir -m 777 analysis_output

  4. Run the Analysis

    1docker run -it --rm \
2  -v "<RDB_FILE_PATH>:/tmp/dump.rdb" \
3  -v "$(pwd)/analysis_output:/engula/analysis_output" \
4  registry.cn-guangzhou.aliyuncs.com/montplex/engula-valuesight

    Replace <RDB_FILE_PATH> with the absolute path to your dump.rdb.

Example Execution

Assuming Docker is installed and your dump.rdb resides in the current directory, execute:

1mkdir analysis_output
2docker pull registry.cn-guangzhou.aliyuncs.com/montplex/engula-valuesight
3
4docker run -it --rm \
5  -v "$(pwd)/dump.rdb:/tmp/dump.rdb" \
6  -v "$(pwd)/analysis_output:/engula/analysis_output" \
7  registry.cn-guangzhou.aliyuncs.com/montplex/engula-valuesight

Runtime Example:

Sample Output:

After the analysis completes, press Enter to view the details or Q / Ctrl+C to exit.

Batch Compression Rate Evaluation

Procedure

  1. Create Required Directories

    1mkdir rdb_dir
2mkdir analysis_output

  2. Organize RDB Files

    • Place one or more .rdb files into rdb_dir (supports nested directories).
    • The tool automatically scans and processes all files ending in .rdb.

  3. Download the Image

    1docker pull registry.cn-guangzhou.aliyuncs.com/montplex/engula-valuesight

  4. Run Batch Processing

    1docker run -it --rm \
2  -v "$(pwd)/rdb_dir:/tmp/rdb_dir" \
3  -v "$(pwd)/analysis_output:/engula/analysis_output" \
4  registry.cn-guangzhou.aliyuncs.com/montplex/engula-valuesight --batch

    ✅ Ensure the rdb_dir and analysis_output mappings correctly reference existing directories.

Shard Aware Evaluation

If an RDB filename ends with an underscore followed by a number, that number is automatically treated as the shard count in the final summary.

Example:

r_profile_100w_1000.rdb

This filename will be recognized as having 1000 shards, improving the accuracy of memory consolidation statistics.

Batch Execution:

Batch Results:

Output Directory Structure

Each .rdb file produces four associated output files:

File Name Description
_dump_1.rdb_cmd_output_for_engula.log Execution log for Engula analysis
_dump_1.rdb_cmd_output_for_redis.log Execution log for Redis evaluation
_dump_1.rdb_engula-stats-rdb.json Parsed statistics from Engula evaluation
_dump_1.rdb_redis-stats-rdb.json Parsed statistics from Redis evaluation

A global info.log is also generated to record high-level process events.

To troubleshoot or verify specific results, consult the corresponding logs in the analysis_output directory.

Running in the Background

Batch analysis across multiple RDB files can take significant time. For extended tasks, execute Engula ValueSight in the background:

1nohup docker run --rm \
2  -v "$(pwd)/rdb_dir:/tmp/rdb_dir" \
3  -v "$(pwd)/analysis_output:/engula/analysis_output" \
4  registry.cn-guangzhou.aliyuncs.com/montplex/engula-valuesight --batch \
5  > analysis_output/docker.log 2> analysis_output/docker.error.log &

Monitor Progress:

1# View runtime output
2tail -f analysis_output/docker.log
3
4# View any error messages
5tail -f analysis_output/docker.error.log

FAQ

Docker Memory Allocation Too Small

Error Message:

Error2: Child process quit abnormally, ExitCode=9

Cause: High memory consumption during large RDB analysis exceeding Docker’s allocated memory.

Resolution:

  • Increase memory limits in Docker Desktop (recommended ≥ 4 GB).
  • Split oversized RDB files and analyze in smaller batches.

Debugging and Logs

After analysis, check diagnostic outputs located in the analysis_output directory. If you encounter unexpected results, include these logs when contacting the Engula technical team.

Docker Installation on Red Hat Linux

On RHEL 9, running:

1yum install docker -y
2docker --version

may yield:

podman version 4.9.4-rhel

which is unsupported.

Follow the official Docker installation guide for proper setup:

  1. Remove Old Versions

    1sudo yum remove docker \
2  docker-client docker-client-latest \
3  docker-common docker-latest \
4  docker-latest-logrotate docker-logrotate \
5  docker-engine podman runc

  2. Install Dependencies and Add Repository

    1sudo yum install -y yum-utils
2sudo yum-config-manager --add-repo https://download.docker.com/linux/rhel/docker-ce.repo

  3. Install the Latest Docker Components

    1sudo yum install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

  4. Start the Docker Service

    1sudo systemctl start docker

Batch Mode: “rdb file not found” Error

This issue occurs when the container isn’t running as root, preventing access to mounted directories.

Fix: Add the --user root flag:

1docker run --user root -it --rm \
2  -v "$(pwd)/rdb_dir:/tmp/rdb_dir" \
3  -v "$(pwd)/analysis_output:/engula/analysis_output" \
4  registry.cn-guangzhou.aliyuncs.com/montplex/engula-valuesight --batch

Contact Us

If you experience issues or want to learn more about Engula or Engula ValueSight, reach out to our support team:

📧 Email: support@montplex.com

Support Hours: Monday – Friday, 09:00 – 18:00 UTC+8 We aim to respond promptly to all technical inquiries. Engula Technical Team — Empowering next‑generation caching performance and observability. 🚀