opentsdb

Pros of Prometheus

• Built-in alerting and query language (PromQL)
• Pull-based architecture, making it easier to monitor ephemeral services
• More active development and larger community support

Cons of Prometheus

• Limited long-term storage options without additional components
• Less scalable for very high cardinality data compared to OpenTSDB

Code Comparison

Prometheus configuration (prometheus.yml):

global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'example'
    static_configs:
      - targets: ['localhost:8080']

OpenTSDB configuration (opentsdb.conf):

tsd.core.auto_create_metrics = true
tsd.storage.hbase.zk_quorum = localhost
tsd.storage.fix_duplicates = true
tsd.http.request.enable_chunked = true
tsd.http.request.max_chunk = 65536

Both Prometheus and OpenTSDB are powerful time-series databases, but they have different architectures and use cases. Prometheus is often preferred for cloud-native environments and microservices, while OpenTSDB excels in handling high-cardinality data and long-term storage. The choice between them depends on specific project requirements and infrastructure.

Pros of InfluxDB

• Built-in HTTP API and query language (InfluxQL) for easier data manipulation
• Native support for tags and fields, allowing more flexible data modeling
• Better out-of-the-box performance for time-series data

Cons of InfluxDB

• Less scalable for extremely large datasets compared to OpenTSDB
• More resource-intensive, especially for memory usage

Code Comparison

InfluxDB query example:

SELECT mean("value") FROM "cpu_load" WHERE "host" = 'server01' AND time >= now() - 1h GROUP BY time(5m)

OpenTSDB query example:

/api/query?start=1h-ago&m=avg:5m-avg:cpu.load{host=server01}

Additional Notes

Both InfluxDB and OpenTSDB are powerful time-series databases, but they have different strengths. InfluxDB offers a more user-friendly experience with its query language and built-in features, making it suitable for a wide range of applications. OpenTSDB, built on top of HBase, excels in handling extremely large datasets and offers better scalability for big data scenarios. The choice between the two depends on specific project requirements, expected data volume, and desired query flexibility.

Pros of Grafana

• More versatile and supports multiple data sources, not limited to time-series data
• Offers a rich, user-friendly interface with customizable dashboards and alerting
• Active development with frequent updates and a large community

Cons of Grafana

• Requires additional setup and configuration for data sources
• Can be resource-intensive for large-scale deployments
• Learning curve for advanced features and query languages

Code Comparison

Grafana (JavaScript):

const panel = new PanelModel({
  type: 'graph',
  title: 'CPU Usage',
  datasource: 'Prometheus',
  targets: [{ expr: 'node_cpu_usage' }]
});

OpenTSDB (Java):

TSQuery query = new TSQuery();
query.setStart("1h-ago");
query.setEnd("now");
query.addSubQuery(new TSSubQuery()
    .setMetric("sys.cpu.user")
    .setAggregator("sum"));

While OpenTSDB focuses on efficient time-series data storage and querying, Grafana provides a more comprehensive visualization and monitoring solution. OpenTSDB excels in handling large volumes of time-series data, but Grafana offers greater flexibility and ease of use for creating dashboards and alerts across various data sources.

Pros of graphite-web

• More user-friendly web interface for data visualization
• Easier to set up and configure for smaller-scale deployments
• Better integration with existing Python-based ecosystems

Cons of graphite-web

• Less scalable for extremely large datasets compared to OpenTSDB
• Limited support for high-cardinality metrics
• Slower query performance for complex aggregations

Code Comparison

graphite-web

from django.conf.urls import url
from . import views

urlpatterns = [
    url('^render/?$', views.renderView, name='render'),
    url('^metrics/find/?$', views.metricsFindView, name='metrics_find'),
]

opentsdb

public class TsdbQuery {
  private final TSDB tsdb;
  private final List<String> tsuids = new ArrayList<String>();
  private final List<String> metrics = new ArrayList<String>();
  private long startTime;
  private long endTime;
}

The code snippets showcase the different approaches and languages used in each project. graphite-web uses Python with Django for URL routing, while OpenTSDB employs Java for its core query functionality.

Pros of TimescaleDB

• Built on PostgreSQL, offering full SQL support and relational database features
• Automatic partitioning and indexing for improved query performance
• Seamless integration with existing PostgreSQL tools and ecosystem

Cons of TimescaleDB

• Higher resource requirements compared to OpenTSDB
• Steeper learning curve for users not familiar with PostgreSQL

Code Comparison

TimescaleDB:

CREATE TABLE metrics (
  time        TIMESTAMPTZ NOT NULL,
  device_id   TEXT,
  temperature DOUBLE PRECISION,
  cpu_usage   DOUBLE PRECISION
);

SELECT time_bucket('1 hour', time) AS hour,
       avg(temperature) AS avg_temp
FROM metrics
WHERE device_id = 'device1'
GROUP BY hour
ORDER BY hour;

OpenTSDB:

tsdb put sys.cpu.user 1356998400 42.5 host=webserver01 cpu=0
tsdb put sys.cpu.user 1356998400 43.2 host=webserver01 cpu=1

tsdb query 1356998400 1356998460 sum sys.cpu.user host=webserver01

TimescaleDB offers a more familiar SQL syntax and relational database features, while OpenTSDB uses a simpler key-value approach for data storage and retrieval. TimescaleDB provides better query flexibility and integration with existing PostgreSQL tools, but may require more resources and have a steeper learning curve compared to OpenTSDB's lightweight and scalable design.

Pros of VictoriaMetrics

• Higher ingestion and query performance, especially for high-cardinality data
• Better storage efficiency, resulting in lower operational costs
• Native support for multi-tenancy and horizontal scalability

Cons of VictoriaMetrics

• Less mature ecosystem compared to OpenTSDB
• Fewer third-party integrations and tools available
• Steeper learning curve for users familiar with OpenTSDB's architecture

Code Comparison

VictoriaMetrics query example:

sum(rate(http_requests_total{job="api-server"}[5m])) by (method)

OpenTSDB query example:

/api/query?start=1h-ago&m=sum:rate:http.requests{job=api-server}&group_by=method

Both systems support similar query capabilities, but VictoriaMetrics uses PromQL-like syntax, which is more expressive and flexible compared to OpenTSDB's query language.

VictoriaMetrics offers better performance and scalability, making it suitable for large-scale deployments. However, OpenTSDB has a more established ecosystem and may be easier to integrate with existing tools. The choice between the two depends on specific requirements, such as performance needs, scalability, and existing infrastructure.