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Side Project2022

Scalable Real-Time Streaming Twitter Data Analytic System

Streaming analytics pipeline that ingests Twitter data with Kafka, processes it through Spark Structured Streaming, stores semantic vectors in Milvus, and runs on Kubernetes for scale and resilience.

PythonKafkaSpark Structured StreamingKubernetesMilvusDocker

At a Glance

Streaming engineSpark
Message busKafka
Vector storeMilvus
OrchestrationKubernetes

The Problem

Trend detection on social data is noisy and time-sensitive. A useful system needs to ingest large tweet volumes continuously, process them with low latency, and capture semantic similarity rather than relying on shallow keyword frequency alone.

Pipeline Architecture

The Streaming Pipeline

Three components form the core write path. Kafka absorbs the raw tweet stream and decouples the source from all downstream consumers. Spark reads micro-batches from Kafka, cleans the text, and generates sentence embeddings using a BERT-family transformer model. Milvus stores the resulting vectors so trend detection can query by semantic similarity rather than keyword frequency.

Kafka — Ingest

  • ·Receives tweet JSON from Streaming API
  • ·Partitions by tweet ID hash for even load
  • ·Persists events for replay and catch-up
→

Spark — Process + Embed

  • ·Micro-batch consumes from Kafka offset
  • ·Cleans and tokenizes tweet text
  • ·Runs BERT model to produce 768-dim vectors
→

Milvus — Store + Search

  • ·Stores embedding vectors with tweet metadata
  • ·HNSW index for approximate nearest-neighbor search
  • ·Returns top-k semantic matches in O(log n)

Data Flow Sequences

The pipeline breaks into two logical flows: tweet ingestion from the Twitter Streaming API through Kafka and Spark into Milvus, and trend detection where semantic similarity search surfaces trending topics. Click any step to inspect the data contract at that stage.

1Stream event: tweet JSON payload2Ack: partition + offset3Poll micro-batch from offset4Batch: tweet records + lag5Tokenize + clean tweet text6Generate sentence embedding (768-dim)7Insert embedding batch8Ack: insert count + internal IDsTwitter Streaming APIKafka BrokerSpark StreamingMilvusAnalytics ClientTwitter Streaming APIKafka BrokerSpark StreamingMilvusAnalytics Client

Key Decisions

  1. 1

    Used Kafka as the core ingestion layer to absorb bursty social traffic while keeping downstream processing decoupled.

  2. 2

    Chose Spark Structured Streaming to express streaming transforms with a mature data-processing model.

  3. 3

    Stored embeddings in Milvus so topic detection could move beyond simple keyword matching into semantic similarity.

  4. 4

    Ran the system on Kubernetes to simplify scaling and recovery across Kafka, Spark, and vector search components.

Outcomes

  • Delivered a working end-to-end pipeline for live tweet ingestion, transformation, semantic indexing, and trend surfacing.
  • Improved topic detection depth by combining streaming analytics with vector similarity search.
  • Created a cloud-native deployment model where each major subsystem could scale independently.

Lessons Learned

  • 1Streaming systems are only as good as their backpressure story; Kafka buys time but does not eliminate downstream bottlenecks.
  • 2Semantic search becomes much more useful when paired with a disciplined ingestion and preprocessing pipeline.
  • 3Kubernetes helps operationalize multi-service analytics systems, but observability must be designed in from the start.
  • 4Real-time demos are compelling, but correctness and recoverability matter more than flashy dashboards.

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