Manajemen Data Deret Waktu
Optimalkan Elasticsearch untuk data deret waktu, termasuk aliran data, ILM (Manajemen Siklus Hidup Indeks), dan arsitektur panas-hangat-dingin.
Manajemen Data Deret Waktu adalah pelajaran Elasticsearch & Full Text Search Systems gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Elasticsearch & Full Text Search Systems, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Elasticsearch & Full Text Search Systems mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
What is Time-Series Data?
Time-series data is information collected over a period of time, often at regular intervals. Think of it as a sequence of data points indexed by time.
Examples include:
- System logs: Events happening on a server.
- IoT sensor readings: Temperature, humidity from devices.
- Financial data: Stock prices over days or hours.
This data is typically append-only and usually immutable once recorded.
Why Elasticsearch for Time-Series?
Elasticsearch is an excellent choice for managing time-series data due to its:
- Scalability: Handles massive volumes of data.
- Speed: Fast indexing and search capabilities.
- Analytics: Powerful aggregations for insights.
- Flexibility: Can store structured and unstructured data.
It's particularly strong for use cases like log analytics, metric monitoring, and security event management.
Introducing Data Streams
Data streams are a core feature in Elasticsearch designed specifically for time-series data. They simplify management by automatically rolling over to new indices as needed.
When you index data into a data stream, it always writes to the current write index. Queries, however, search across all backing indices transparently. This makes managing large, continuously growing datasets much easier.
Creating Your First Data Stream
To create a data stream, you first need an index template that defines its structure and points to a data stream.
Then, simply indexing a document into the stream's name will create it automatically based on the template:
PUT _index_template/my-data-stream-template
{
"index_patterns": ["my-data-stream-*"],
"data_stream": {},
"priority": 200,
"template": {
"settings": {
"number_of_shards": 1
},
"mappings": {
"properties": {
"@timestamp": {
"type": "date",
"format": "strict_date_optional_time||epoch_millis"
},
"message": { "type": "text" }
}
}
}
}
// Indexing a document creates the stream
POST my-data-stream/_doc
{
"@timestamp": "2023-10-27T10:00:00Z",
"message": "Sensor reading: 25.5C"
}Automating with ILM
Index Lifecycle Management (ILM) is a powerful feature that automates the management of your indices through various phases of their life.
For time-series data, ILM helps you:
- Automatically roll over indices when they reach a certain size or age.
- Move older, less frequently accessed data to cheaper storage.
- Delete data that is no longer needed.
The Four ILM Phases
ILM policies define actions for indices across four main phases:
- Hot: Indices are actively being written to and frequently queried. Requires fast storage.
- Warm: Indices are no longer being written to, but are still queried. Can be read-only and potentially on slower storage.
- Cold: Indices are rarely queried and often stored on very cheap, slow storage.
- Delete: Indices are no longer needed and are safely removed from the cluster.
Crafting an ILM Policy
Here's an example of an ILM policy that defines rollover, forcemerge (for warm phase), and deletion stages for time-series data:
PUT _ilm/policy/my-ts-policy
{
"policy": {
"phases": {
"hot": {
"actions": {
"rollover": {
"max_age": "7d",
"max_docs": 10000000,
"max_size": "50gb"
}
}
},
"warm": {
"min_age": "30d",
"actions": {
"forcemerge": {
"max_num_segments": 1
},
"shrink": {
"number_of_shards": 1
}
}
},
"cold": {
"min_age": "90d",
"actions": {
"freeze": {}
}
},
"delete": {
"min_age": "180d",
"actions": {
"delete": {}
}
}
}
}
}Hot-Warm-Cold Architecture
A Hot-Warm-Cold architecture optimizes resource utilization and cost for time-series data by using different types of nodes for different ILM phases.
- Hot nodes: Use fast CPUs, SSDs for high indexing and query throughput.
- Warm nodes: Use less powerful CPUs, HDDs (or slower SSDs) for older, read-only data.
- Cold nodes: Use very cheap, high-capacity storage, potentially even object storage, for rarely accessed data.
Assigning Node Roles
You can configure nodes to serve specific roles (hot, warm, cold) by setting node.attr in their elasticsearch.yml configuration file.
ILM policies then use these attributes to automatically move indices to the appropriate node types as they transition through phases.
# For a hot node
node.roles: [ data ]
node.attr.data: hot
# For a warm node
node.roles: [ data ]
node.attr.data: warm
# For a cold node
node.roles: [ data ]
node.attr.data: coldILM & Data Stream Check
Which of the following statements about Elasticsearch's time-series features are TRUE?
Time-Series Recap
Great job! You've explored how Elasticsearch is optimized for time-series data.
We covered:
- Data Streams: Simplifying index management and rollovers.
- ILM (Index Lifecycle Management): Automating index transitions through hot, warm, cold, and delete phases.
- Hot-Warm-Cold Architectures: Optimizing cluster resources and costs by assigning different node types to specific ILM phases.
These features are essential for building scalable and cost-effective time-series solutions with Elasticsearch.
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Pertanyaan yang Sering Diajukan
Apakah pelajaran “Manajemen Data Deret Waktu” gratis?
Ya — teks lengkap “Manajemen Data Deret Waktu” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Elasticsearch & Full Text Search Systems, upgrade ke CoddyKit PRO. Kursus Elasticsearch & Full Text Search Systems mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Manajemen Data Deret Waktu”?
Optimalkan Elasticsearch untuk data deret waktu, termasuk aliran data, ILM (Manajemen Siklus Hidup Indeks), dan arsitektur panas-hangat-dingin. Kamu berlatih Elasticsearch & Full Text Search Systems dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
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Semua pelajaran dalam kursus ini
- Kemampuan Pencarian Geospasial
- Manajemen Data Deret Waktu
- Strategi Penerapan Produksi
- Manajemen Siklus Hidup Indeks