Implementing Sharding and Scaling

1. Sharding by Hash

Property	Description	Values	Notes
sharding.algorithm	Algorithm used for sharding	`hash`	Default sharding method in Prometheus
shard.key	Label used to determine shard	`instance`	Can be any label present on metrics
shard.count	Number of shards to create	`4`	Must be a power of 2 for optimal distribution

Example: Configuring Hash Sharding

sharding:
  algorithm: hash
  shard_key: instance
  shard_count: 4

Note: Hash sharding ensures consistent distribution of metrics across shards, reducing contention and improving query performance.

2. Sharding by Target

Property	Description	Values
sharding.strategy	Defines the sharding strategy for target distribution	`hash`, `range`
sharding.key	Key used for sharding logic (e.g., target labels)	`instance`, `job`, custom label

Example: Configuring sharding by target label

scrape_configs:
  - job_name: "sharded-metrics"
    scrape_interval: 15s
    sharding:
      strategy: hash
      key: instance

Note: Sharding by target ensures that each Prometheus instance only scrapes a subset of targets, reducing load and improving scalability.

3. Using Modulus Relabeling

Modulus relabeling is a technique used in Prometheus to distribute metrics across multiple instances by hashing metric labels and assigning them to specific shards based on a modulus operation.

Label	Description	Usage
__shard__	Calculated shard ID based on the modulus of a label value.	Used internally by Prometheus to route metrics to the correct shard.
__modulus__	Configurable value that determines the number of shards.	Set in Prometheus configuration to define shard distribution.

Example: Configuring Modulus Relabeling

scrape_configs:
  - job_name: "sharded-metrics"
    static_configs:
      - targets: ['localhost:9090']
    relabel_configs:
      - source_labels: [__address__]
        target_label: __shard__
        modulus: 4

Note: The modulus parameter determines how many shards the metrics will be distributed across. Ensure that all Prometheus instances in the sharding setup use the same modulus value to maintain consistency.

4. Implementing Horizontal Scaling

Example: Configuring Prometheus for Horizontal Scaling

# prometheus.yml
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: "prometheus"
    static_configs:
      - targets: ["localhost:9090"]

rule_files:
  - "rules.yml"

remote_write:
  - url: "http://remote-prometheus-1:9090/api/v1/write"
    queue_config:
      capacity: 1000
      max_samples_per_send: 100
      batch_send_deadline: 5s

remote_read:
  - url: "http://remote-prometheus-1:9090/api/v1/read"

Configuration Option	Description	Default Value
remote_write.url	URL to write samples to	None
remote_write.queue_config.capacity	Maximum number of samples to queue	1000
remote_write.queue_config.max_samples_per_send	Maximum samples per send request	100
remote_write.queue_config.batch_send_deadline	Max time to wait before sending a batch	5s
remote_read.url	URL to read samples from	None

Note: Horizontal scaling in Prometheus is achieved through remote storage and federation.

Horizontal Scaling Strategies

Remote Storage: Write samples to external systems like Thanos or Cortex
Federation: Use one Prometheus instance to scrape and forward metrics to another
Sharding: Split the target list across multiple Prometheus instances

Steps to Implement Horizontal Scaling

Set up a remote storage backend (e.g., Thanos, Cortex)
Configure remote_write in Prometheus to send data to the backend
Configure remote_read to fetch data from the backend
Use prometheus.yml to define job targets and scrape intervals
Scale Prometheus instances horizontally by sharding target lists

5. Configuring Shard Count

Parameter	Description	Default Value	Notes
shard.count	Number of shards to use for data distribution	`1`	Must be a positive integer. Increasing this value increases parallelism but also resource usage.
shard.strategy	Strategy used to distribute data across shards	`hash`	Options: `hash`, `range`, `round-robin`

Example: Setting Shard Count in Configuration

sharding:
  count: 4
  strategy: hash

Note: The shard count should be chosen based on the expected data volume and query patterns to ensure optimal performance and resource utilization.

6. Using Service Discovery Filtering

Service discovery filtering in Prometheus allows you to narrow down the targets that are scraped by using label-based matching. This is particularly useful in environments with many services where you want to focus on specific subsets.

Filter Type	Description	Example
static_config	Defines a static list of targets with labels.	`{"targets": ["localhost:9090"], "labels": {"job": "prometheus"}}`
file_sd_config	Uses a file to define targets and labels.	`[{"targets": ["localhost:9090"], "labels": {"job": "prometheus"}}]`
consul_sd_config	Discovers targets from Consul.	`{"server": "localhost:8500", "services": ["prometheus"]}`
kubernetes_sd_config	Discovers targets from Kubernetes.	`{"role": "pod", "namespace": "monitoring"}`

Example: Filtering by Label in Prometheus Configuration

scrape_configs:
  - job_name: "prometheus"
    static_configs:
      - targets: ["localhost:9090"]
        labels:
          job: "prometheus"
          env: "production"

  - job_name: "node_exporter"
    static_configs:
      - targets: ["localhost:9100"]
        labels:
          job: "node_exporter"
          env: "production"

  - job_name: "filtered_exporter"
    static_configs:
      - targets: ["localhost:9100"]
        labels:
          job: "node_exporter"
          env: "staging"

Note: Label-based filtering is applied at the job level. You can use relabeling rules to further refine which targets are scraped.

7. Implementing Dynamic Sharding

Component	Description	Configuration
sharding.strategy	Defines the sharding approach used by Prometheus	`hash`, `range`
sharding.key	Key used for sharding logic (e.g., metric name)	`__name__`, `job`
sharding.replica	Replica identifier for sharded instances	`replica=0`

Example: Dynamic sharding configuration

scrape_configs:
  - job_name: "prometheus"
    static_configs:
      - targets: [localhost:9090]
    relabel_configs:
      - source_labels: [__address__]
        target_label: replica
        replacement: "0"

Note: Dynamic sharding requires a consistent hashing algorithm to ensure that metrics are consistently routed to the same shard.

Warning: Misconfiguring sharding keys can lead to data inconsistency or loss.

Sharding Strategy	Description	Use Case
hash	Uses hash-based distribution to assign metrics to shards	High cardinality metrics
range	Assigns metrics based on a range of values	Metrics with predictable value ranges

8. Balancing Shard Load

Strategy	Description	Implementation	Notes
Consistent Hashing	Maps metrics to shards using a hash ring for even distribution	`func (s Shard) GetShardForKey(key string) Shard { hash := fnv1a.HashString(key) for _, shard := range s.shards { if hash <= shard.MaxHash { return shard } } return s.shards[0] }`	Reduces shard movement during scaling
Dynamic Resharding	Automatically redistributes data when shards become unbalanced	`sharding: strategy: dynamic threshold: 0.15 rebalance_interval: 30s`	Triggers when load exceeds threshold
Load Metrics	Tracks shard CPU, memory, and query rate to detect imbalance	`rate(prometheus_tsdb_head_samples_appended_total[5m])`	Used to calculate shard load ratio

Example: Load Balancing Configuration

sharding:
  strategy: consistent_hash
  shards:
    - name: shard-1
      host: http://prometheus-1:9090
    - name: shard-2
      host: http://prometheus-2:9090

Note: Consistent hashing ensures minimal data movement when shards are added or removed.

Warning: Overloading a shard can cause query timeouts and data loss.

Load Balancing Best Practices

Monitor shard CPU and memory usage regularly
Use consistent hashing to minimize data reshuffling
Set thresholds for rebalancing to avoid frequent resharding

9. Monitoring Shard Performance

Metric	Description	Unit	Notes
prometheus_tsdb_head_chunks	Number of chunks in the head block	chunks	Indicates memory usage and ingestion rate
prometheus_tsdb_head_series	Number of series in the head block	series	Shows active time series count
prometheus_tsdb_wal_truncations	Total number of WAL truncations	count	High values may indicate performance issues
prometheus_tsdb_compaction_duration_seconds	Duration of compaction in seconds	seconds	Measures time to compact chunks into blocks
prometheus_tsdb_blocks_loaded	Number of blocks loaded into memory	blocks	Indicates how many block files are currently loaded

Example: Querying Head Series Count


prometheus_tsdb_head_series

Example: Monitoring Compaction Duration


prometheus_tsdb_compaction_duration_seconds

Note: Use these metrics to detect performance bottlenecks in sharded environments.

10. Using Thanos for Scaling

Example: Deploying Thanos with Prometheus

prometheus:
  config:
    remote_write:
      - url: "http://thanos-remote-write:19291/api/v1/receive"
        send_exemplars: true
    remote_read:
      - url: "http://thanos-query:10902/api/v1/read"

thanos:
  query:
    endpoint:
      - "http://prometheus:9090"
  store:
    endpoint:
      - "http://prometheus:9090"

Component	Description	Notes
thanos-query	Aggregates and queries data from multiple Prometheus instances	Must be configured with store endpoints
thanos-store	Stores and serves long-term metrics data	Can be configured with object storage (S3, GCS)
thanos-compact	Compacts and downsamples long-term data	Runs periodically to reduce storage size
thanos-ruler	Runs alerting rules across multiple Prometheus instances	Can be deployed in a distributed fashion

Note: Thanos requires a shared object storage backend (e.g., S3, GCS) to store long-term metrics and metadata.

Warning: Ensure that all Prometheus instances are configured with consistent remote write endpoints to avoid data loss.

Thanos Scaling Benefits

Horizontal scaling of Prometheus instances
Long-term storage with object storage
Global query view across multiple Prometheus instances
High availability through distributed architecture

11. Implementing Cross-Shard Queries

When working with sharded Prometheus instances, cross-shard queries require special handling to ensure data consistency and performance. These queries span multiple shards and often involve aggregations across different Prometheus instances.

Query Type	Description	Notes
Cross-shard aggregation	Aggregates metrics from multiple shards using PromQL	Use `sum by (instance)` or similar to combine results
Shard-aware query routing	Directs queries to specific shards based on labels or metrics	Requires external query routers like Thanos or Cortex
Global query federation	Collects and federates metrics from all shards into a single view	May introduce latency due to data collection overhead

Example: Cross-shard metric aggregation

sum by (job, instance) (
  rate(http_requests_total[5m])
)

Note: When using cross-shard queries, ensure that the same metric labels are consistent across shards to avoid incorrect aggregations.

Warning: Queries that span multiple shards may experience higher latency than local queries, especially in high-latency network environments.

Key Considerations for Cross-Shard Queries

Label consistency across shards is critical for accurate results.
Use query routers like Thanos or Cortex to simplify cross-shard queries.
Aggregations should be performed at the shard level where possible to reduce cross-shard data transfer.
Monitor query performance and latency when querying across shards.

12. Planning Capacity for Shards

Resource	Description	Recommended Allocation
Storage	Amount of disk space required per shard	100GB - 500GB per shard depending on data retention
Memory	RAM needed for each shard's in-memory data structures	2GB - 8GB per shard
CPU	Processing power for scraping and querying	2 - 4 vCPUs per shard
Network	Bandwidth for scraping targets and inter-shard communication	100 Mbps - 1 Gbps per shard

Example: Shard Capacity Estimation

# Estimate storage needs based on metrics per second
METRICS_PER_SECOND=10000
RETENTION_DAYS=30
STORAGE_PER_METRIC=256 bytes

TOTAL_STORAGE_GB=$(echo "scale=2; ($METRICS_PER_SECOND * 86400 * $RETENTION_DAYS * 256) / (1024*1024*1024)" | bc)
echo "Estimated storage per shard: ${TOTAL_STORAGE_GB} GB"

Note: Shard capacity planning should consider both historical data and real-time scraping load.

Key Considerations

Storage capacity must accommodate data retention policies
Memory allocation affects query performance and caching
CPU allocation impacts scraping and aggregation efficiency
Network bandwidth affects inter-shard communication and data sync