AI/ML '25

• GenAI
    - Text: Chat, Q&A, Compose, Summarize, Think, Search, Insights, Research
     - Image: Gen, Identify, Search (Image-Image, Text-Image, etc), Label, Multimodal
    - Code gen
    - Research: Projects, Science, Breakthroughs
    - MoE

• Agentic
    - Workflows: GenAI, DNN, Scripts, Tools, etc combined to fulfil Objectives
        -- Auto-Generated Plans & Objectives  
    - Standardization: MCP (API), Interoperability, Protocols
    - RAG
    - Tools: Websearch, DB, Invoke API/ Tools/ LLM, etc

• Context
    - Fresh/ Updated
    - Length: Cost vs Speed trade-off
    - RAG
    - VectorDB (Similarity/ Relevance)
    - Memory enhanced

• Fine Tune
    - Foundation models (generalists) -> Specialists
    - LoRA
    - Inference time scaling (compute, tuning, etc)
    - Prompts

• Multimodal: Text, Audio, Video, Image, Graph, Sensors

• Safety/ Security
    - Output Quality: Relevance, Accuracy, Correctness, Evaluation (Automated Rating, Ranking, JudgeLLM, etc)
        -- Hallucination
    - Privacy, Data Leak, Backdoor, Jailbreak
    - Guard Rails

AI Agentic Frameworks

With prolification of AI Agents, it's only logical that there will be attempts at standardization and building protocols & frameworks:

• MCP covered previously
• Any-Agent from Mozilla.ai to switch between agents, vendors, clouds, etc
• Agent2Agent interoperability protocol

On Quantization

• Speed vs Accuracy trade off.
• Reduce costs on storage, compute, operations .
• Speed up output generation, inference, etc.
• Work with lower precision data.
• Cast/ map data from Int32, Float32, etc 32-bit or higher precision to lower precision data types such as 16-bit Brain Float (BFloat16) or 4-bit (NFloat)/ int4 or int8, etc.
• East mapping Float32 (1-bit Sign, 7-bit Exponent, 23-bit Mantissa) => BFloat16 (1-bit Sign, 7-bit Exponent, 7-bit Mantissa). Just discard the higher 16-bits of mantissa. No overflow!
• Straightforward mapping work out max, min, data distribution, mean, variance, etc & then sub-divide into equally sized buckets based on bit size of the lower precision data type. E.g int4 (4-bit) => 2^4 = 16 buckets. 
  
• Handle outliers, data skew which can mess up the mapping, yet lead to loss of useful info if discarded randomly.
• Work out Bounds wrt Loss of Accuracy.

LLMs, AI/ ML side:

• https://newsletter.theaiedge.io/p/reduce-ai-model-operational-costs

Lucene, Search side:

• https://www.elastic.co/search-labs/blog/scalar-quantization-101
• https://www.elastic.co/search-labs/blog/scalar-quantization-in-lucene

Speculative Decoding

• Ensemble of Weak + Strong model
  
• Weak model has a quick first go at generating tokens/ inference (potentials)
• Followed by the Strong, but slow model which catches up & uses the outputs of the weak model, samples them, grades them, accepting/ rejecting them to generate the final output
• Overall making inferences via LLMs quicker and cheaper

More to follow..

• https://pytorch.org/blog/hitchhikers-guide-speculative-decoding/ 
• https://www.baseten.co/blog/a-quick-introduction-to-speculative-decoding/
• https://research.google/blog/looking-back-at-speculative-decoding/
• https://medium.com/ai-science/speculative-decoding-make-llm-inference-faster-c004501af120

Revisiting the Bitter Lesson

Richard Sutton's - The Bitter Lesson(s) continue to hold true. Scaling/ data walls could pose challenges to scaling AI general purpose methods (like searching and learning) beyond a point. And that's where human innovation & ingenuity would be needed. But hang on, wouldn't that violate the "..by our methods, not by us.." lesson?

Perhaps then something akin to human innovation/ discovery/ ingenuity/ creativity might be the next frontier of meta-methods. Machines in their typical massively parallel & distributed, brute-force, systematic trial & error fashion would auto ideate/ innovate/ discover solutions quicker, cheaper, better. Over & over again.

So machine discoveries shall be abound, just not Archimedes's Eureka kind, but Edison's 100-different ways style!

Model Context Protocol (MCP)

Standardization Protocol for AI agents. Enables them to act, inter-connect, process, parse, invoke functions. In other words to Crawl, Browse, Search, click, etc. 

MCP re-uses well known client-server architecture using JSON-RPC. 

Apps use MCP Clients -> MCP Servers (abstracts the service)

Kind of API++ for an AI world!

Open Weight AI

Inspired by Open Source Software (OSS), yet not fully open...

With Open Weight (OW) typically the final model weights (& the fully trained model) are made available under a liberal free to reuse, modify, distribute, non-discriminating, etc licence. This helps for anyone wanting to start with the fully trained Open Weight model & apply them, fine-tune, modify weights (LoRA, RAG, etc) for custom use-cases. To that extent, OW has a share & reuse philosophy.
 
On the other hand, wrt training data, data sources, detailed architecture, optimizations details, and so on OW diverges from OSS by not making it compulsory to share any of these. So these remain closed source with the original devs, with a bunch of pros & cons. Copyright material, IP protection, commercial gains, etc are some stated advantages for the original devs/ org. But lack of visibility to the wider community, white box evaluation of model internals, biases, checks & balances are among the downsides of not allowing a full peek into the model.

Anyway, that's the present, a time of great flux. As models stabilize over time OW may tend towards OSS...

References

• https://openweight.org/    
• https://www.oracle.com/artificial-intelligence/ai-open-weights-models/
• https://medium.com/@aruna.kolluru/exploring-the-world-of-open-source-and-open-weights-ai-aa09707b69fc
• https://www.forbes.com/sites/adrianbridgwater/2025/01/22/open-weight-definition-adds-balance-to-open-source-ai-integrity/
• https://promptengineering.org/llm-open-source-vs-open-weights-vs-restricted-weights/
• https://promptmetheus.com/resources/llm-knowledge-base/open-weights-model
• https://www.agora.software/en/llm-open-source-open-weight-or-proprietary/

The Big Book of LLM

A book by Damien Benveniste of AIEdge. Though a work in progress, chapters 2 - 4 available for preview are fantastic. 

Look forward to a paperback edition, which I certainly hope to own...

Mozilla.ai

Mozilla pedigree, AI focus, Open-source, Dev oriented.

Blueprint Hub: Mozilla.ai's Hub of open-source templtaized customizable AI solutions for developers.

Lumigator: Platform for model evaluation and selection. Consists a Python FastAPI backend for AI lifecycle management & capturing workflow data useful for evaluation.

Streamlit

Streamlit is a web wrapper for Data Science projects in pure Python. It's a lightweight, simple, rapid prototyping web app framework for sharing scripts.

• https://streamlit.io/playground
• https://www.restack.io/docs/streamlit-knowledge-streamlit-vs-flask-vs-django
• https://docs.streamlit.io/develop/concepts/architecture/architecture
• https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit

Scaling Laws

Quick notes around Chinchilla Scaling Law/ Limits & beyond for DeepLearning and LLMs.

Factors

• Model size (N)
• Dataset size (D)
• Training Cost (aka Compute) (C)
• Test Cross-entropy loss (L)

The intuitive way,

• Larger data will need a larger model, and have higher training cost. In other words, N, D, C all increase together, not necessarily linearly, could be exponential, log-linear, etc.
• Likewise Loss is likely to increase for larger datasets. So an inverse relationship between L & D (& the rest).
• Tying them into equations would be some constants (scaling, exponential, alpha, beta, etc), unknown for now (identified later).
  

Beyond common sense, the theoretical foundations linking the factors aren't available right now. Perhaps the nature of the problem is it's hard (NP).

The next best thing then, is to somehow work out the relationships/ bounds empirically. To work with existing Deep Learning models, LLMs, etc using large data sets spanning TB/ PB of data, Trillions of parameters, etc using large compute budget cumulatively spanning years.

Papers by Hestness & Narang, Kaplan, Chinchilla are all attempts along the empirical route. So are more recent papers like Mosaic, DeepSeek, MoE, Llam3, Microsoft among many others. 

Key take away being,

• The scale & bounds are getting larger over time. 
• Models from a couple of years back, are found to be grossly under-trained in terms of volumes of training data used. They should have been trained on an order of magnitude larger training data for an optimal training, without risk of overfitting.
• Conversely, the previously used data volumes are suited to much smaller models (SLMs), with inference capabilities similar to those older LLMs.

References

• https://en.wikipedia.org/wiki/Neural_scaling_law
• https://lifearchitect.ai/chinchilla/
• https://medium.com/@raniahossam/chinchilla-scaling-laws-for-large-language-models-llms-40c434e4e1c1
• https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
• https://medium.com/nlplanet/two-minutes-nlp-scaling-laws-for-neural-language-models-add6061aece7
• https://lifearchitect.ai/the-sky-is-bigger/

Diffusion Models

Diffusion

•     Forward, Backward (Learning), Sampling (Random)    
•     Continous Diffusion
•     VAE, Denoising Autoencoder
•     Markov Chains
•     U-Net
•     DALL-E (OpenAI), Stable Diffusion,
•     Imagen, Muse, VEO (Google)
•     LLaDa, Mercury Coder (Inception)

Non-equilibrium Thermodynamics

•     Langevin dynamics
•     Thermodynamic Equilibrium - Boltzmann Distribution
•     Wiener Process - Multidimensional Brownian Motion
•     Energy Based Models

Gaussian Noise

•     Denoising
•     Noise/ Variance Schedule
•     Derivation by Reparameterization

Variational Inference    

•     Denoising Diffusion Probabilistic Model (DDPM)
•     Noise Prediction Networks    
•     Denoising Diffusion Implicit Model (DDIM)

Loss Functions

•     Variational Lower Bound (VLB)
•     Evidence Lower Bound (ELBO)
•     Kullback-Leibler divergence (KL divergence)
•     Mean Squared Error (MSE)

Score Based Generative Model

•     Annealing
•     Noise conditional score network (NCSN)
•     Equivalence: DDPM and Score BBased Generative Models

Conditional (Guided) Generation

•     Classifier Guidance    
•     Classifier Free Guidance (CFG)

Latent Varible Generative Model

•     Latent Diffusion Model (LDM)
•     Lower Dimension (Latent) Space

References:

• https://en.wikipedia.org/wiki/Diffusion_model
• https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction
• https://www.ibm.com/think/topics/diffusion-models
• https://hackernoon.com/what-is-a-diffusion-llm-and-why-does-it-matter
• Large Language Diffusion Models (LLaDA): https://arxiv.org/abs/2502.09992
  

Mechanistic Interpretability

• Clearer better understanding of Neural Networks working (white box).
• Strong grounds for Superposition: n-dimensions (neurons) represent more than n-features

References

• https://dynalist.io/d/n2ZWtnoYHrU1s4vnFSAQ519J#z=EuO4CLwSIzX7AEZA1ZOsnwwF
• https://www.neelnanda.io/mechanistic-interpretability/glossary
• https://transformer-circuits.pub/2022/toy_model/index.html
• https://www.anthropic.com/research/superposition-memorization-and-double-descent
• https://transformer-circuits.pub/2023/toy-double-descent/index.html 

State Space Models

• Vector Space of States (of the System)
  
• Alt. to Transformers, reducible to one another 

 

        (Image source: https://en.wikipedia.org/wiki/State-space_representation)

References

• https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mamba-and-state
• https://huggingface.co/blog/lbourdois/ssm-2022
• https://huggingface.co/blog/lbourdois/get-on-the-ssm-train
• https://en.wikipedia.org/wiki/State-space_representation

Spark API Categorization

A way to categorize Spark API features:

• Flow of data is generally across the category swim lanes, from creation of a New Spark Context to reading data using I/O to Filter, Map/ Transform, Reduce/ Agg etc Action.
• Lazy processing upto Transformation.
• Steps only get executed once an Action is invoke.
• Post Actions (Reduce, Collect, etc) there could again be I/O, thus the reverse flow from Action
• Partition is a cross cutting concern across all layers. For I/O, Transformations, Actions could be across all or a few Partitions.
• forEach on the Stream could be at either at Transform or Action levels.
  

The diagram is based on code within various Spark test suites. 

Mocked Kinesis (Localstack) with PySpark Streaming

Continuing with the same PySpark (ver 2.1.0, Python3.5, etc.) setup explained in an earlier post. In order to connect to the mocked Kinesis stream on Localstack from PySpark use the kinesis_wordcount_asl.py script located in Spark external/ (connector/) folder.

(a) Update value of master in kinesis_wordcount_asl.py

Update value of master(local[n], spark://localhost:7077, etc) in SparkContext in kinesis_wordcount_asl.py:
    sc = SparkContext(appName="PythonStreamingKinesisWordCountAsl",master="local[2]")

(b) Add aSpark compiled jars to Spark Driver/ Executor Classpath

As explained in step (III) of an earlier post, to work with Localstack a few changes were done to the KinesisReceiver.scala onStart() to explicitly set endPoint on kinesis, dynamoDb, cloudWatch clients. Accordingly the compiled aSpark jars with the modifications need to be added to Spark Driver/ Executor classpath.

• For Spark local mode (master="local[n]"): additions to classpath can be exported in the SPARK_CLASSPATH variable.

     export aSPARK_PROJ_HOME="/Downlaod/Location/aSpark"
    export SPARK_CLASSPATH="${aSPARK_PROJ_HOME}/target/original-aSpark_1.0-2.1.0.jar:${aSPARK_PROJ_HOME}/target/scala-2.11/classes:${aSPARK_PROJ_HOME}/target/scala-2.11/jars/*"

•  For Spark Standalone mode: "spark.executor.extraClassPath" needs to be set in either spark-defaults.conf or added as a SparkConf to SparkContext (see (II)(a))

(c) Ensure SPARK_HOME, PYSPARK_PYTHON & PYTHONPATH variables are exported.

(d) Run kinesis_wordcount_asl

    python3.5 ${SPARK_HOME}/external/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py SampleKinesisApplication myFirstStream http://localhost:4566/ us-east-1

• put-records to Localstack Kinesis

    aws  --endpoint-url=http://localhost:4566 kinesis put-record --stream-name myFirstStream --partition-key 123 --data "testdata abcd"

• Count of the words streamed (put) will show up on the kinesis_wordcount_asl console

 

Spark Streaming with Kinesis mocked on Localstack

In this post we get a Spark streaming application working with AWS Kinesis stream, a mocked version of Kinesis running locally on Localstack. In earlier posts we have explained how to get Localstack running and various AWS services up on Localstack. The client connections to AWS services (Localstack) is done using AWS cli and AWS Java-Sdk v1.

Environment: This set-up continues on a Ubuntu20.04, with Java-8, Maven-3.6x, Docker-24.0x, Python3.5, PySpark/ Spark-2.1.0, Localstack-3.8.1, AWS Java-Sdk-v1 (ver.1.12.778),

Once the Localstack installation is done, steps to follow are:

(I) Start Localstack
    # Start locally
    localstack start

    That should get Localstack should be running on: http://localhost:4566

(II) Check Kinesis services from CLI on Localstack

    # List Streams
    aws --endpoint-url=http://localhost:4566 kinesis list-streams

    # Create Stream
    aws --endpoint-url=http://localhost:4566 kinesis create-stream --stream-name myFirstStream --shard-count 1

    # List Streams
    aws --endpoint-url=http://localhost:4566 kinesis list-streams

    # describe-stream-summary
    aws --endpoint-url=http://localhost:4566 kinesis describe-stream-summary --stream-name myFirstStream

    # Put Record
    aws  --endpoint-url=http://localhost:4566 kinesis put-record --stream-name myFirstStream --partition-key 123 --data "testdata abcd"
    aws  --endpoint-url=http://localhost:4566 kinesis put-record --stream-name myFirstStream --partition-key 123 --data "testdata efgh"

(III) Connect to Kinesis from Spark Streaming

• Download & Build a sample aSpark - Java Kinesis application.
• The code is similar to Spark's kinesis-asl from external (connector) module. Except for a few changes to KinesisReceiver.scala onStart() method to explicitly set endPoint on kinesis, dynamoDb, cloudWatch clients. This enables Localstack endPoint url to be plugged into kinesis, dynamoDb & cloudwatch.

    # Build
    mvn install -DskipTests=true -Dcheckstyle.skip

    # Run JavaKinesisWordCountASL with Localstack

• JavaKinesisWordCountASL SampleKinesisApplication myFirstStream http://localhost:4566/
  

• runJavaKinesisWordCountASL.sh script located in sbin/ folder of the aSpark project can be used to run JavaKinesisWordCoundASL from the shell
  

(IV) Add Data to Localstack Kinesis & View Counts on Console
    a) Put Record from cli
    aws  --endpoint-url=http://localhost:4566 kinesis put-record --stream-name myFirstStream --partition-key 123 --data "testdata abcd"
    aws  --endpoint-url=http://localhost:4566 kinesis put-record --stream-name myFirstStream --partition-key 123 --data "testdata efgh"

    b) Alternatively Put records from Java Kinesis application
    Download, build & run AmazonKinesisRecordProducerSample.java
    
    c) Now check the output console of JavaKinesisWordCountASL run in step (III) above. Counts of the words streamed from Localstack Kinesis will be displayed on the console.

Debugging Spark Scala/ Java components

In continuation to the earlier post regarding debugging Pyspark, here we show how to debug the Spark Scala/ Java side. Spark is a distributed processing environment and has Scala Api's for connecting from different languages like Python & Java. The high level Pyspark Architecture is shown here.

For debugging the Spark Scala/ Java components as these run within the JVM, it's easy to make use of Java Tooling Options for remote debugging from any compatible IDE such as Idea (Eclipse longer supports Scala). A few points to remember:

• Multiple JVMs in Spark: Since Spark is a distributed application, it involves several components like the Master/ Driver, Slave/ Worker, Executor. In a real world truly distributed setting, each of the components runs in its own separate JVM on separated Physical machines. So be clear about which component you are exactly wanting to debug & set up the Tooling options accordingly targetting the specific JVM instance.

• Two-way connectivity between IDE & JVM: At the same time there should be a two-way network connectivity between the IDE (debugger) & the running JVM instance

• Debugging Locally: Debugging is mostly a dev stage activity & done locally. So it may be better to debug on a a Spark cluster running locally. This could be either on a Spark Spark cluster or a Spark run locally (master=local[n]/ local[*]).

Steps:

Environment: Ubuntu-20.04 having Java-8, Spark/Pyspark (ver 2.1.0), Python3.5, Idea-Intelli (ver 2024.3), Maven3.6

(I) Idea Remote JVM Debugger
In Idea > Run/ Debug Config > Edit > Remote JVM Debug.

• Start Debugger in Listen to Remote JVM Mode
• Enable Auto Restart

(II)(a) Debug Spark Standlone cluster
Key features of the Spark Standalone cluster are:

• Separate JVMs for Master, Slave/ Worker, Executor
• All could run on a single dev box, provided enough resources (Mem, CPU) are available
• Scripts inside SPARK_HOME/sbin folder like start-master.sh, start-slave.sh (start-worker.sh), etc to start the services

In order to Debug lets say some Executor, a Spark Standalone cluster could be started off with 1 Master, 1 Worker, 1 Executor.   

    # Start Master (Check http://localhost:8080/ to get Master URL/ PORT)
    ./sbin/start-master.sh 

    # Start Slave/ Worker
    ./sbin/start-slave.sh spark://MASTER_URL:<MASTER_PORT>

    # Add Jvm tooling to extraJavaOption to spark-defaults.conf
    spark.executor.extraJavaOptions  -agentlib:jdwp=transport=dt_socket,server=n,address=localhost:5005,suspend=n

    # The value could instead be passed as a conf to SparkContext in Python script:
    from pyspark.conf import SparkConf
    confVals = SparkConf()
    confVals.set("spark.executor.extraJavaOptions","-agentlib:jdwp=transport=dt_socket,server=n,address=localhost:5005,suspend=y")
    sc = SparkContext(master="spark://localhost:7077",appName="PythonStreamingStatefulNetworkWordCount1",conf=confVals)

(II)(b) Debug locally with master="local[n]"

• In this case a local Spark cluster is spun up via scripts like spark-shell, spark-submit, etc. located inside the bin/ folder
• The different components Master, Worker, Executor all run within one JVM as threads, where the value n is the no of threads, (set n=2)
• Export JAVA_TOOL_OPTIONS before in the terminal from which the Pyspark script will be run

        export JAVA_TOOL_OPTIONS="-agentlib:jdwp=transport=dt_socket,server=n,suspend=n,address=5005"

(III) Execute PySpark Python script
    python3.5 ${SPARK_HOME}/examples/src/main/python/streaming/network_wordcount.py localhost 9999

This should start off the Pyspark & connect the Executor JVM to the waiting Idea Remote debugger instance for debugging.

Debugging Pyspark in Eclipse with PyDev

An earlier post shows how to run Pyspark (Spark 2.1.0) in Eclipse (ver 2024-06 (4.32)) using the PyDev (ver 12.1) plugin. The OS is Ubuntu-20.04 with Java-8, & an older version of Python3.5 compatible with PySpark (2.1.0).

While the Pyspark code runs fine within Eclipse, when trying to Debug an error is thrown:

    Pydev: Unexpected error setting up the debugger: Socket closed". 

This is due to a higher Python requirement (>3.6) for pydevd debugger module within PyDev. Details from the PyDev installations page clearly state that Python3.5 is compatible only with PyDev9.3.0. So it's back to square one.

Install/ replace Pydev 12.1 with PyDev 9.3 in Eclipse

• Uninstall Pydev 12.1 (Help > About > Installation details > Installed software > Uninstall PyDev plugin)

• Also manually remove all Pydev folders from eclipse/plugins folder (com.python.pydev.* & org.python.pydev.*)

• Download & Install PyDev 9.3 from Download location as per instructions
  

• Unzip to eclipse/dropins folder

• Restart eclipse & check (Help > About > Installation details > Installed software)

Test debugging Pyspark
Refer to the steps to Run Pyspark on PyDev in Eclipse, & ensure the PyDev Interpreter is python3.5, PYSPARK_PYTHON variable and PYTHONPATH are correctly setup.

Finally, right click on network_wordcount.py > Debug as > Python run
(Set up Debug Configurations > Arguments & provide program arguments, e.g. "localhost 9999", & any breakpoints in the python code to test).

 

Pyspark in Eclipse with PyDev

This post captures the steps to get Spark (ver 2.1) working within Eclipse (ver 2024-06 (4.32)) using the PyDev (ver 12.1) plugin. The OS is Ubuntu-20.04 with Java-8, Python 3.x & Maven 3.6.

(I) Compile Spark code

The Spark code is downloaded & compiled from a location "SPARK_HOME".

    export SPARK_HOME="/SPARK/DOWNLOAD/LOCATION"

    cd ${SPARK_HOME}

    mvn install -DskipTests=true -Dcheckstyle.skip -o

(Issue: For a "Failed to execute goal org.scalastyle:scalastyle-maven-plugin:0.8.0:check":
Copy scalastyle-config.xml to the sub-project (next to pom.xml) having the error.

(II) Compile Pyspark
    (a) Install Pyspark dependencies

• Install Pandoc

        sudo apt-get install pandoc

• Install a compatible older Pypandoc (ver 1.5)

        pip3 install pypandoc==1.5

• Install a compatible older Python 3.x (ver 3.5)

        sudo add-apt-repository ppa:deadsnakes/ppa

        sudo apt-get install python3.5
    
    (b) Build Pyspark

        cd ${SPARK_HOME}/python

        export PYSPARK_PYTHON=python3.5

        # Build - creates ${SPARK_HOME}/python/build
        python3.5 setup.py

        # Dist - creates ${SPARK_HOME}/python/dist
        python3.5 setup.py sdist

    (c) export PYTHON_PATH

    export PYTHONPATH=$PYTHONPATH:${SPARK_HOME}/python/:${SPARK_HOME}/python/lib/py4j-0.10.4-src.zip:${SPARK_HOME}/python/pyspark/shell.py;
    
(III) Run Pyspark from console
    Pyspark setup is done & stanalone examples code should run. Ensure variables ${SPARK_HOME}, ${PYSPARK_PYTHON} & ${PYTHONPATH} are all correctly exported (steps (I), (II)(b) & (II)(c) above):

    python3.5 ${SPARK_HOME} /python/build/lib/pyspark/examples/src/main/python/streaming/network_wordcount.py localhost 9999

(IV) Run Pyspark on PyDev in Eclipse

    (a) Eclipse with PyDev plugin installed:
    Set-up tested on Eclipse (ver 2024-06 (4.32.0)) and PyDev plugin (ver 12.1x).
 
    (b) Import the spark project in Eclipse
    There would be compilation errors due to missing Spark Scala classes.

    (c) Add Target jars for Spark Scala classes
    Eclipse no longer has support for Scala so the corresponding Spark Scala classes are missing. A work around is to add the Scala target jars compiled using mvn (in step (I) above) manually to: 

        spark-example > Properties > Java Build Path > Libraries
   

    (d) Add PyDev Interpreter for Python3.5
    Go to: spark-example > Properties > PyDev - Interpreter/ Grammar > Click to confure an Interpreter not listed > Open Interpreter Preferences Page > New > Choose from List:  

    & Select /usr/bin/python3.5

 

 On the same page, under the Environment tab add a variable named "PYSPARK_PYTHON" having value "python3.5"

    (e) Set up PYTHONPATH for PyDev

    spark-example > Properties > PyDev - PYTHONPATH

• Under String Substitution Variables add a variable with name "SPARK_HOME" & value "/SPARK/DOWNLOAD/LOCATION" (same location added in Step (I)). 

 

• Under External Libraries, Choose Add based on variable, add 3 entries:
  

                 ${SPARK_HOME}/python/

                 ${SPARK_HOME}/python/lib/py4j-0.10.4-src.zip

                 ${SPARK_HOME}/python/lib/py4j-0.10.4-src.zip

   With that Pyspark should be properly set-up within PyDev.

    (f) Run Pyspark from Eclipse

    Right click on network_wordcount.py > Run as > Python run
    (You can further change Run Configurations > Arguments & provide program arguments, e.g. "localhost 9999")

Scala IDE no more

Sad that Scala IDE for Eclipse is no longer supported. While it was a great to have Scala integrated within Eclipse, guess the headwinds were too strong!

Working with Moto & Lambci Lambda Docker Images

Next up on Mock for clouds is Moto. Moto is primarily for running tests within the Python ecosystem.

Moto does offer a standalone server mode for a other langauges. General sense was that the standalone Moto server would offer the AWS services which will be accessible from the cli & non-Python SDKs. Gave Moto a shot with the same AWS services tried with Localstack.

(I) Set-up

While installing Moto ran into a couple of dependency conflicts across moto, boto3, botocore, requests, s3transfer & in turn with the installed awscli. With some effort reached a sort of dynamic equillibrium with (installed via pip):

• awscli                       1.36.11             
• boto3                        1.35.63             
• botocore                   1.35.70             
• moto                         5.0.21              
• requests                   2.32.2                          
• s3transfer                0.10.4  

(II) Start Moto Server

    # Start Moto
    moto_server -p3000

    # Start Moto as Docker (Sticking to this option)
    docker run --rm -p 5000:5000 --name moto motoserver/moto:latest

(III) Invoke services on Moto

    (a) S3
    # Create bucket
    aws --endpoint-url=http://localhost:5000 s3 mb s3://test-buck

    # Copy item to bucket
    aws --endpoint-url=http://localhost:5000 s3 cp a1.txt s3://test-buck

    # List bucket
    aws --endpoint-url=http://localhost:5000 s3 ls s3://test-buck

--
    (b) SQS
    # Create queue
    aws --endpoint-url=http://localhost:5000 sqs create-queue --queue-name test-q

    # List queues
    aws --endpoint-url=http://localhost:5000 sqs list-queues

    # Get queue attribute
    aws --endpoint-url=http://localhost:5000 sqs get-queue-attributes --queue-url http://localhost:5000/123456789012/test-q --attribute-names All

--
    (c) IAM
    ## Issue: Moto does a basic check of user role & gives an AccessDeniedException when calling Lambda CreateFunction operation
    ## So have to create a specific IAM role (https://github.com/getmoto/moto/issues/3944#issuecomment-845144036) in Moto for the purpose.

    aws iam --region=us-east-1 --endpoint-url=http://localhost:5000 create-role --role-name "lambda-test-role" --assume-role-policy-document "some policy" --path "/lambda-test/"

--
    (d) Lambda
    # Create Java function
    aws --endpoint-url=http://localhost:5000 lambda create-function --function-name test-j-div --zip-file fileb://original-java-basic-1.0-SNAPSHOT.jar --handler example.HandlerDivide::handleRequest --runtime java8.al2 --role arn:aws:iam::123456789012:role/lambda-test/lambda-test-role

    # List functions
    aws --endpoint-url=http://localhost:5000 lambda list-functions

    # Invoke function (Fails!)
    aws --endpoint-url=http://localhost:5000 lambda invoke --function-name test-j-div --payload '[235241,17]' outputJ.txt

    The invoke function fails with the message:
    "WARNING - Unable to parse Docker API response. Defaulting to 'host.docker.internal'
    <class 'json.decoder.JSONDecodeError'>::Expecting value: line 1 column 1 (char 0)
    error running docker: Error while fetching server API version: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory'))".
    
    Retried this from AWS Java-SDK & for other nodejs & python function but nothing worked. While this remains unsolved for now, check out Lambci docker option next.

(IV) Invoke services on Lambci Lambda Docker Images:

    Moto Lambda docs also mention its dependent docker images from the lambci/lambda & mlupin/docker-lambda (for new ones). Started off with a slightly older java8.al2 docker image from lambci/lambda.

    # Download lambci/lambda:java8.al2
    docker pull lambci/lambda:java8.al2
    
    # Run lambci/lambda:java8.al2.   
    ## Ensure to run from the location which has the unzipped (unjarred) Java code
    ## Here it's run from a folder called data_dir_java which has the unzipped (unjarred) class file folders: com/, example/, META-INF/, net/ 

    docker run -e DOCKER_LAMBDA_STAY_OPEN=1 -p 9001:9001 -v "$PWD":/var/task:ro,delegated --name lambcijava8al2 lambci/lambda:java8.al2 example.HandlerDivide::handleRequest

    # Invoke Lambda
    aws --endpoint-url=http://localhost:9001 lambda invoke --function-name test-j-div --payload '[235241,17]' outputJ.txt

    This works!
 

AWS Lambda on Localstack using Java-SdK-v1

Continuing with Localstack, next is a closer look into the code to deploy and execute AWS Lambda code on Localstack from AWS Java-Sdk-v1. The localstack-lambda-java-sdk-v1 code uses the same structure used in localstack-aws-sdk-examples & fills in for the missing AWS Lambda bit.

The LambdaService class has 3 primary methods - listFunctions(), createFunction() & invokeFunction().  The static AWSLambda client is setup with Mock credentials and pointing to the Localstack endpoint.
 
The main() method first creates the function (createFunction()), if it does not exist.

• It builds a CreateFunctionRequest object with the handler, runtime, role, etc specified
• It also reads the jar file of the Java executable from the resources folder into a FunctionCode object & adds it to the CreateFunctionRequest
• Next a call is made to the AWSLambda client createFunction() with the CreateFunctionRequest which hits the running Localstack instance (Localstack set-up explained earlier).

If all goes well, control returns to main() which invokes the listFunctions() to show details of the created Lambda function (& all others functions existing).

Finally, there is call from main() to invokeFunction() method.

• Which invokes the recently created function with a InvokeRequest object filled with some test values as the payload.
• The response from the invoked function is a InvokeResult object who's payload contains the results of the lambda function computation.

Comments welcome, localstack-lambda-java-sdk-v1 is available to play around!

 

Getting Localstack Up and Running

In continuation to the earlier post on mocks for clouds, this article does a deep dive into getting up & running with Localstack. This is a consolidation of the steps & best practices shared here, here & here. The Localstack set-up is on a Ubuntu-20.04, with Java-8x, Maven-3.8x, Docker-24.0x. 

(I) Set-up

    # Install awscli
     sudo apt-get install awscli

    # Install localstack ver 3.8
        ## Issue1: By default pip pulls in version 4.0, which gives an error:
        ## ERROR: Could not find a version that satisfies the requirement localstack-ext==4.0.0 (from localstack) 

        python3 -m pip install localstack==3.8.1

--

    # Add to /etc/hosts
    127.0.0.1    localhost.localstack.cloud
    127.0.0.1    s3.localhost.localstack.cloud

--

    # Configure AWS from cli
    aws configure
    aws configure set default.region us-east-1
    aws configure set aws_access_key_id test
    aws configure set aws_secret_access_key test

    ## Manually configure AWS
    Add to ~/.aws/config
    endpoint_url = http://localhost:4566

    ## Add mock credentials
    Add to ~/.aws/credentials
    aws_access_key_id = test
    aws_secret_access_key = test

--

    # Download docker images needed by the Lambda function
        ## Issue 2: Do this before hand, Localstack gets stuck
        ## at the download image stage unless it's already available

    ## Pull java:8.al2
    docker pull public.ecr.aws/lambda/java:8.al2

    ## Pull nodejs (required for other nodejs Lambda functions)
    docker pull public.ecr.aws/lambda/nodejs:18

    ## Check images downloaded
    docker image ls

(II) Start Localstack

    # Start locally
    localstack start

    # Start as docker (add '-d' for daemon)
       ## Issue 3: Local directory's mount should be as per sample docker-compose

    docker-compose -f docker-compose-localstack.yaml up

    # Localstack up on URL's
    http://localhost:4566
    http://localhost.localstack.cloud:4566

    # Check Localstack Health
    curl http://localhost:4566/_localstack/info
    curl http://localhost:4566/_localstack/health

(III) AWS services on Localstack from CLI

  (a) S3
    # Create bucket named "test-buck"
    aws --endpoint-url=http://localhost:4566 s3 mb s3://test-buck

    # Copy item to bucket
    aws --endpoint-url=http://localhost:4566 s3 cp a1.txt s3://test-buck

    # List bucket
    aws --endpoint-url=http://localhost:4566 s3 ls s3://test-buck

--

  (b) Sqs
    # Create queue named "test-q"
    aws --endpoint-url=http://localhost:4566 sqs create-queue --queue-name test-q

    # List queues
    aws --endpoint-url=http://localhost:4566 sqs list-queues

    # Get queue attribute
    aws --endpoint-url=http://localhost:4566 sqs get-queue-attributes --queue-url http://sqs.us-east-1.localhost.localstack.cloud:4566/000000000000/test-q --attribute-names All

--

  (c) Lambda
    aws --endpoint-url=http://localhost:4566 lambda list-functions

    # Create Java function
    aws --endpoint-url=http://localhost:4566 lambda create-function --function-name test-j-div --zip-file fileb://original-java-basic-1.0-SNAPSHOT.jar --handler example.HandlerDivide::handleRequest --runtime java8.al2 --role arn:aws:iam::000000000000:role/lambda-test

    # List functions
    aws --endpoint-url=http://localhost:4566 lambda list-functions

    # Invoke Java function
    aws --endpoint-url=http://localhost:4566 lambda invoke --function-name test-j-div --payload '[200,9]' outputJ.txt

    # Delete function
    aws --endpoint-url=http://localhost:4566 lambda delete-function --function-name test-j-div

(IV) AWS services on Localstack from Java-SDK

    # For S3 & Sqs - localstack-aws-sdk-examples, java sdk

    # For Lambda - localstack-lambda-java-sdk-v1

 

Killing me softly

With your air. With your smog. With your AQIs. With your chart topping PM levels. Delhi this annual event of yours, wish we could skip!

Familiar noises echoing from the four estates are no balm to the troubled sinuses. They shout at the top of their lungs, we cough & sneeze from the bottom of ours.

Solution, now what's that? From whom, when, where & why? Since one's can't really run away perhaps we need to just hibernate or hide. Better still, grin and bear this way of lieF (sic). 

Mutable Argument Capture with Mockito

There are well known scenarios like caching, pooling, etc wherein object reuse is common. Testing these cases using a framework like Mockito could run into problems. Esp if there's a need to verify the arguments sent by the Caller of a Service, where the Service is mocked.

ArgumentCaptor (mockito) fails because it keeps references to the argument obj, which due to reuse by the caller only have the last/ latest updated value.    
The discussion here led to using Void Answer as one possible way to solve the issue. The following (junit-3+, mockito-1.8+, commons-lang-2.5) code explains the details.

1. Service: 

public class Service {

public void serve(MutableInt value) {

System.out.println("Service.serve(): "+value);

}

....

 

2. Caller:

public class Caller {

public void callService(Service service) {

MutableInt value = new MutableInt();

value.setValue(1);

service.serve(value);

value.setValue(2);

service.serve(value);

}

...

 

3.Tests:

public class MutableArgsTest extends TestCase{

List<MutableInt> multiValuesWritten;

@Mock

Service service;

 

/**

* Failure with ArgumentCaptor

*/

public void testMutableArgsWithArgCaptorFail() {

Caller caller = new Caller();

ArgumentCaptor<MutableInt> valueCaptor = 

ArgumentCaptor.forClass(MutableInt.class);

caller.callService(service);

verify(service,times(2)).serve(valueCaptor.capture());

// AssertionFailedError: expected:<[1, 2]> but was:<[2, 2]>"

assertEquals(Arrays.asList(new MutableInt(1), 

new MutableInt(2)),valueCaptor.getAllValues());

}

 

        /**

* Success with Answer

*/

public void testMutableArgsWithDoAnswer() {

Caller caller = new Caller();

doAnswer(new CaptureArgumentsWrittenAsMutableInt<Void>()).

when(service).serve(any(MutableInt.class));

caller.callService(service);

verify(service,times(2)).serve(any(MutableInt.class));

// Works!

assertEquals(new MutableInt(1),multiValuesWritten.get(0));

assertEquals(new MutableInt(2),multiValuesWritten.get(1));

}

/**

* Captures Arguments to the Service.serve() method:

* - Multiple calls to serve() happen from the same caller

* - Along with reuse of MutableInt argument objects by the caller

* - Argument value is copied to a new MutableInt object & that's captured

* @param <Void>

*/

public class CaptureArgumentsWrittenAsMutableInt<Void> implements Answer<Void>{

public Void answer(InvocationOnMock invocation) {

Object[] args = invocation.getArguments();

multiValuesWritten.add(new MutableInt(args[0].toString()));

return null ;

}

}

}

Restore Joomla 4 Manually

This post has info on manually restoring a Joomla 4.3.4 set-up across two servers. While both are Linux systems the configuration differ slightly including the OS, Php , DB, etc. Various issues were faced & overcome in doing the restoration.

Background info:

- Source:
Ubuntu 22.04, Php 8.2, Joomla 4.3.4, Apache, Maria DB, Addon Plugins (AddToAny, LazyDb, Komento, SexyPolling)

- Destination:
Ubuntu 20.04, Php 7.4, Joomla 4.3.4, Apache, MySql 8.0

- Latest DB dump & htdocs folder (including all files, modules, plugins, media, images etc.) from Source was transferred to Destination server via Ftp before hand.

Steps:

1) DB Import

    1.1) Create user, db, grant all permission to user. 

    1.2) Import data to the created db from the latest DB dump of the source.
 
        1.2.1) ERROR 1366 (HY000) at line 2273: Incorrect integer value: '' for column 'checked_out' at row 1. Solution is to set NO_ENGINE_SUBSTITUTION & then import:

            SET @@GLOBAL.sql_mode= 'NO_ENGINE_SUBSTITUTION';

    1.3) ERROR 1101 (42000) at line 10692: BLOB, TEXT, GEOMETRY or JSON column 'country' can't have a default value

        - Using the solution found online & the DB dump sql import script was changed to set DEFAULT values for the problematic text columns country, city, etc
    

         // Modify the sexypolling plugin CREATE TABLE script:
            CREATE TABLE `#_sexy_votes` (
              `id_vote` int(10) unsigned NOT NULL AUTO_INCREMENT,
                  ....
              `country` text NOT NULL DEFAULT (_utf8mb4'Unknown'),
              `city` text NOT NULL DEFAULT (_utf8mb4'Unknown'),
              `region` text NOT NULL DEFAULT (_utf8mb4'Unknown'),
              `countrycode` text NOT NULL DEFAULT (_utf8mb4'Unknown'),
              PRIMARY KEY (`id_vote`),
                  .....

            ) ENGINE=MyISAM DEFAULT CHARSET=utf8mb3 COLLATE=utf8mb3_general_ci;

2) Download Joomla_4.3.4-Stable-Full_Package.zip from joomla.org
    2.1) Unzip Joomla_4.3.4-Stable-Full_Package.zip to /var/www/html & rename folder to <site_name>

    2.2) Set up site configuration.php (/var/www/html/<site_name>/configuration.php)
        - Add db, username, password
        - Add tmp_path & log_path in

        public $log_path = '/var/www/html/<site_name>/administrator/logs';
          public $tmp_path = '/var/www/html/<site_name>/tmp';

3) Restore Joomla modules, plugins, languages, etc from file system Ftp backup location of Source.

4) Additional system settings on Destination

    4.1) Add missing Php modules: "Call to undefined function" error

        4.1.1) simplexml_load_file()
            sudo apt-get install php7.4-xml

        4.1.2) "IntlTimeZone" module missing
            sudo apt-get install php7.4-intl

    4.2) Increase Php upload limit (/etc/php/7.4/apache2/php.ini)

          post_max_size = 38M
            upload_max_filesize = 32M

    4.3) Restart apache
          sudo systemctl reload apache2
 

5) Recovering from J4 Red Error Page of death

    5.1) Redirection to installation/index.php:

    - With an error "500 - Whoops, looks like something went wrong".
    - Needed to delete the installation folder, to stop the redirection.

    5.2) Next, 404 Component not found error on the home page: 
 

    ---
        404 **Component not found.**

        Call stack
        #     Function     Location
        1     ()     JROOT/libraries/src/Component/ComponentHelper.php:296
        2     Joomla\CMS\Component\ComponentHelper::renderComponent()     JROOT/libraries/src/Application/SiteApplication.php:210
        3     Joomla\CMS\Application\SiteApplication->dispatch()     JROOT/libraries/src/Application/SiteApplication.php:251
        4     Joomla\CMS\Application\SiteApplication->doExecute()     JROOT/libraries/src/Application/CMSApplication.php:293
        5     Joomla\CMS\Application\CMSApplication->execute()     JROOT/includes/app.php:61
        6     require_once()     JROOT/index.php:32
    ---
 

    5.3) Checked DB connections using a custom php script:

No issues connecting to DB with username/ password!

    5.4) Enable Debugging/ Logging:
        

        5.4.1) Logging in php.ini (/etc/php/7.4/apache2/php.ini)
            ----Turn on logging-----
               display_errors = On
               html_errors = On
               display_startup_errors = On
               log_errors = On
               error_log = /var/log/apache2/php_errors.log

        5.4.2) Logging in configuration.php (/var/www/html/<site_name>/configuration.php)
            // Change to true from false
                public $debug = true;
                public $debug_lang = true;

                // Change to 'maximum' from 'default'
                public $error_reporting = 'maximum';
    
                // Change to 1 from 0
                public $log_everything = 1;

    With those J! Info started showing up in the browser along with the error stack trace & queries.

    5.5) Root cause analysis

        5.5.1) Checked the specific php libraries:
            libraries/src/Component/ComponentHelper.php:296
            libraries/src/Application/SiteApplication.php:210, etc..

    - Using var_dump($component), on SiteApplication.php:210 found: 

           $component = NULL

        - The same '$component = "com_content"' on the home page of a default Joomla application (unzip Joomla 4.3.4 zip & install & check value on Joomla home page).

        - Test with hard coded $component = "com_content" in libraries/src/Application/SiteApplication.php:210

                if(empty($component)){
                     $component = "com_content";
                }

        - With this 404 was gone & a broken site home page came up with a few Category links listed out

        - Clicking on Category link was showing that "No Article linked to Category", despite there being several Articles imported from source db dump.

        5.5.2) Localizing issue with Content/ Article loading:
        - Hit the direct Article url:
            http://<site_name>/index.php?option=com_content&view=article&id=<article_id>

        - This gave another error"404 Article not found", though the specific <article_id> was present in the database.

        - J! Info provided the corresponding php file and db query used to fetch article by id which was giving no result

    5.5.3) Issue with imports of all "datetime DEFAULT NULL" fields

    - On exploring the query further, it was seen to have checks for publish_up & publish_down dates. These needed to be either NULL or set to a date earlier (/later) than date NOW for publish_up (/publish_down).

    - In the "#_content" table publish_up & publish_down dates values were showing as "0000-00-00 00:00:00" (i.e. were imported as 0) in place of NULL.
This was causing all records being filtered out.

    - It also meant that wherever the "datetime default NULL" fields were imported the same issue was happening. 

        - A check revealed 30 other J! tables with the same issue.
 
        - Prepared a script to update each of these datetime fields to NULL in the 30 tables.
            UPDATE `#_content` SET `checked_out_time` = NULL, `publish_up` = NULL, `publish_down` = NULL;

            UPDATE `#_categories` SET `checked_out_time` = NULL;
                                                                     .... for all the affected tables!

With that the issue was resolved & site home page became functional!