Mapping in Java Options

•

1 like•1,195 views

Anna Pietras

Short summary about most popular mappers for java appliacation.

Software

Why to use mappers?
- default settings -> less coding
- ‘global’ settings for handling NULLS, empty String ect.
- Simple conversions handling (lists, enums, different numeric types) ‘out of
the box’
- Centralization of mapping configuration in application

Available options
Source: http://stackoverflow.com/questions/1432764/any-tool-for-java-object-to-object-mapping/1432956
● Transmorph
● EZMorph
● Commons-BeanUtils
● Commons-Lang
● Commons-Convert
● Morph
● Lorentz
● Dozer
● ModelMapper
● OTOM
● Smooks
● MapStruct
● Orika
● JMapper

Available options
Library Dozer Orika JMapper
Version 5.5.1 1.4.6 1.4.2
Last release 04/2014 05/2015 09/2015
Configuration XML, annotation
(beta), JavaConfig
JavaConfig annotation, XML
Enum<>String yes yes no
Collections yes, + Map<>Pojo yes yes
Custom mappers yes, class/field yes, class/field yes, class/field

Dozer
● Reflection based (==slowly)
● Mapping from map to Object
● Develop since 2006r.
● Most popular
● Not actively maintained (152
issues on GitHub)
● stackOverflow: 1519 result
XML
ANNOTATION
JavaConfig

Orika
- Generates bytecode during first
mapping (first time is slow, then
faster)
- 27 open issues on GH
- Last commit 2 months ago
- stackOverflow: 211 results
- Developed since 2012
JavaConfig

Jmapper
- Generates bytecode during
application startup
- Last commit one month old
- 5 open issues on GH
- Dynamic converter declaration
(code snippets, including xml)
- Field mapping 1:n and n:1
- Support for flattening and
encapsulation
- StackOverflow: 19 results
- Developed since 2013
@GlobalJMap
ANNOTATIONS
XML

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This presentation covers the following topics: What is logging? The purpose of logging: Debugging The purpose of logging: Security The purpose of logging: Stats & analytics Traditional logging Traditional logging: Advantages Traditional logging: Disadvantages The solution: Large-scale logging Large-scale logging: Core principles Large-scale logging: Solution types Large-scale logging: Cloud vs on-prem Large-scale logging: Operational complexity Large-scale logging: Security Large-scale logging: Costs Large-scale logging: On-prem comparison - Elasticsearch - Grafana Loki - VictoriaLogs On-prem comparison: Setup and operation On-prem comparison: Costs On-prem comparison: Full-text search support On-prem comparison: How to efficiently query 100TB of logs? On-prem comparison: Integration with CLI tools VictoriaLogs for large-scale logging VictoriaLogs demo instance - Ingestion rate: 3600 messages / minute - The number of log messages: 1.1 billion - Uncompressed log messages’ size: 1.5TB - Compressed log messages’ size: 23GB - Compression ratio: 47x - Memory usage: 150MB VictoriaLogs CLI integration demo - Which errors have occurred in all the apps during the last hour? - How many errors have occurred during the last hour? - Which apps generated the most of errors during the last hour? - The number of per-minute errors for the last 10 minutes - Status codes for the last hour - Non-200 status codes for the last week - Top client IPs for the last 4 weeks with 404 and 500 response status codes - Per-month stats for the given IP across all the logs Large-scale logging solution MUST provide excellent CLI integration VictoriaLogs: (temporary) drawbacks VictoriaLogs: Recap - Easy to setup and operate - The lowest RAM usage and disk space usage (up to 30x less than Elasticsearch and Grafana Loki) - Fast full-text search - Excellent integration with traditional command-line tools for log analysis - Accepts logs from popular log shippers (Filebeat, Fluentbit, Logstash, Vector, Promtail, Grafana Agent) - Open source and free to use!

Large-scale Logging Made Easy: Meetup at Deutsche Bank 2024

VictoriaMetrics

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