Big data processing is a key pillar of core competitiveness for modern enterprises. An effective server-side processing solution must address the entire data collection, storage, computation, and analysis process. To address the challenges of massive data volumes, server architectures must meet core requirements for high throughput, low latency, and scalability, while also ensuring the accuracy and integrity of the data being generated.

Data Collection and Access Layer

Data collection is the first step in big data processing, requiring real-time access to heterogeneous data from multiple sources. Log collection tools such as Flume and Logstash enable real-time collection and transmission of server logs, supporting a variety of data sources and destinations. The message queue system Kafka, serving as a data buffer, offers high throughput and durability, effectively handling data spikes. For real-time synchronization of structured data, tools such as Canal and Debezium implement change data capture (CDC) by parsing database logs.

java
// Kafka Producer Example
Properties props = new Properties();
props.put("bootstrap.servers", "kafka1:9092,kafka2:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
Producer<String, String> producer = new KafkaProducer<>(props);
ProducerRecord<String, String> record = new ProducerRecord<>("data-topic", "key", "value");
producer.send(record);
producer.close();

The data ingestion layer also needs to consider data format standardization and preliminary cleansing. Serialization formats such as Avro and Protobuf not only save storage space but also enable schema evolution. Data validation rules should be implemented during the ingestion phase to filter out invalid data and malicious requests, ensuring the quality of downstream processing.

Distributed Storage Solutions

HDFS, as the cornerstone of traditional big data storage, is suitable for storing massive amounts of unstructured data. Its high fault tolerance and high throughput provide stable support for batch processing jobs. Object storage such as S3 and OSS are increasingly becoming the preferred solutions for data lakes, offering unlimited scalability and cost advantages. Columnar storage formats such as Parquet and ORC excel in analytical scenarios, significantly reducing I/O operations and improving query performance.

Storage strategies should be flexibly designed based on data popularity. Hot data should be stored on high-performance SSDs, warm data on HDDs, and cold data can be archived to lower-cost storage media. Data lifecycle management policies should automate data migration and cleanup to optimize storage costs.

Python
Parquet File Reading and Writing Examples
import pyarrow.parquet as pq
import pandas as pd
Writing Parquet
df = pd.DataFrame({'id': [1, 2, 3], 'value': ['A', 'B', 'C']})
pq.write_table(pa.Table.from_pandas(df), 'data.parquet')
Reading Parquet
table = pq.read_table('data.parquet')
df = table.to_pandas()

Data partitioning and indexing strategies are crucial for query performance. Partitioning by time range is a common practice, while also considering data distribution. Indexing technologies such as Bloom filters accelerate point queries and avoid full table scans.

Batch Processing Framework

MapReduce, as a first-generation big data computing framework, is suitable for offline batch processing scenarios, but it has high disk I/O overhead. Spark, with its in-memory computing and DAG execution engine, significantly improves batch processing performance, supporting a variety of workloads, including SQL queries, stream processing, and machine learning. Emerging frameworks such as Flink have also demonstrated excellent performance in batch processing, particularly in event-time processing and state management.

scala
// Spark WordCount Example
val textFile = spark.sparkContext.textFile("hdfs://...")
val counts = textFile.flatMap(line => line.split(" "))
.map(word => (word, 1))
.reduceByKey(_ + _)
counts.saveAsTextFile("hdfs://...")

Query optimization is the core of batch processing systems. Predicate pushdown reduces data reads, column pruning avoids unnecessary I/O, and dynamic partition pruning optimizes join operations. Execution plan tuning requires considering data characteristics and cluster resources to appropriately set parallelism and memory allocation.

Stream Processing Technology Stack

The growing demand for real-time stream processing requires systems with low latency and high availability. Spark Streaming provides a micro-batch processing mode, ensuring exactly-once processing semantics and sharing a codebase with batch jobs. Flink, as a true stream processing framework, supports event time processing and complex event detection, and performs outstandingly in real-time ETL and monitoring scenarios.

java
// Flink stream processing example
DataStream<String> text = env.socketTextStream("localhost", 9999);
DataStream<Tuple2<String, Integer>> counts = text
.flatMap(new Tokenizer())
.keyBy(0)
.window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
.sum(1);
counts.print();

The integrated stream-batch architecture is becoming a trend. The same set of APIs supports both stream processing and batch processing, simplifying the technology stack and ensuring consistency in processing logic. Both Flink and Spark are continuously evolving in this regard, providing a unified computing experience.

Resource Management and Scheduling

YARN, as the resource manager of the Hadoop ecosystem, supports multi-tenancy and multiple computing frameworks. Mesos provides more flexible resource allocation strategies, suitable for mixed workload scenarios. Kubernetes, with its powerful container orchestration capabilities, has become the scheduler of choice for cloud-native big data platforms.

Resource scheduling strategies must balance fairness and efficiency. The capacity scheduler ensures that each queue receives a minimum resource guarantee, while the fair scheduler dynamically allocates resources. Preemption mechanisms prevent resource starvation and improve cluster utilization.

Elastic scaling dynamically adjusts resources based on load, reducing costs while maintaining SLAs. Metric-based autoscaling (HPA) responds to load changes in real time, while scheduled scaling addresses periodic business peaks.

Data Governance and Quality

Metadata management is the foundation of data governance, providing data lineage tracking and impact analysis. Data lineage tracking helps understand data flow paths, while impact analysis assesses the scope of change impacts. The data catalog enables the discovery and understanding of data assets, improving data utilization.

Data quality monitoring must cover dimensions such as completeness, accuracy, consistency, and timeliness. The rule engine defines quality verification rules and automatically performs data quality assessments. Anomaly detection algorithms identify abnormal data patterns and provide timely alerts on data quality issues.

Data security mechanisms include encryption, desensitization, and access control. Transmission encryption prevents data theft, while storage encryption protects data at rest. Dynamic desensitization replaces sensitive information during queries, and the principle of least privilege limits data access.

Operations and Maintenance Monitoring System

Cluster monitoring covers hardware resources, service status, and business metrics. Prometheus collects monitoring metrics, and Grafana provides visualization. The ELK stack is a common choice for centralized log collection and analysis. Link tracing analyzes request processing paths and identifies performance bottlenecks.

Automated operations improve management efficiency. Configuration management tools such as Ansible and Chef ensure environmental consistency, and CI/CD pipelines automate deployment processes. Disaster recovery solutions ensure business continuity, cross-data center replication provides data redundancy, and failover mechanisms quickly restore services.

Use PromQL to query cluster status
100 - (avg by (instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)
sum(container_memory_usage_bytes{container_label_org_label_schema_group="spark"})

Performance tuning is an ongoing process. Benchmarking establishes a performance baseline, and stress testing identifies system bottlenecks. JVM tuning optimizes garbage collection, and network tuning reduces data transmission latency. Query optimization rewrites execution plans, and index optimization accelerates data access.

Server-side big data processing solutions require flexible selection based on data scale, real-time requirements, and business scenarios. The traditional Hadoop ecosystem is mature and stable, cloud-native architectures are flexible and elastic, and integrated batch and stream processing simplifies the technology stack. With hardware advancements and algorithm innovations, big data processing technology will continue to evolve, providing stronger support for enterprise digital transformation.