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Kubernetes for Big Data Workloads

Abhishek Tiwari

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Key challenges. Performance.

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Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices

The Morning Paper

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., When a QoS violation is predicted to occur and a culprit microservice located, Seer uses a lower level tracing infrastructure with hardware monitoring primitives to identify the reason behind the QoS violation.

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Why MySQL Could Be Slow With Large Tables

Percona

Example: Creating four simple tables to store strings but using different data types: db1 test> CREATE TABLE tb1 (id int auto_increment primary key, test_text char(200)); Query OK, 0 rows affected (0.11 sec) db1 test> CREATE TABLE tb3 (id int auto_increment primary key, test_text tinytext); Query OK, 0 rows affected (0.13