Time Series Databases: InfluxDB, TimescaleDB, and Modern Data Storage

from dataclasses import dataclass
from typing import Dict, List, Optional, Any
from datetime import datetime

@dataclass
class DataPoint:
    measurement: str
    timestamp: datetime
    fields: Dict[str, float]
    tags: Dict[str, str] = None
    
    def __post_init__(self):
        if self.tags is None:
            self.tags = {}

@dataclass
class TimeSeriesQuery:
    measurement: str
    start_time: datetime
    end_time: Optional[datetime] = None
    fields: List[str] = None
    tags: Dict[str, str] = None
    aggregation: str = None
    granularity: str = None
    
    def to_dict(self) -> Dict:
        return {
            'measurement': self.measurement,
            'start': self.start_time.isoformat(),
            'end': self.end_time.isoformat() if self.end_time else None,
            'fields': self.fields,
            'filters': self.tags,
            'aggregation': self.aggregation,
            'granularity': self.granularity
        }

class TimeSeriesModel:
    def __init__(self):
        self.data_points: List[DataPoint] = []
    
    def add_point(self, measurement: str, timestamp: datetime, 
                  fields: Dict[str, float], tags: Dict[str, str] = None):
        point = DataPoint(measurement, timestamp, fields, tags)
        self.data_points.append(point)
    
    def get_measurement_names(self) -> List[str]:
        return list(set(p.measurement for p in self.data_points))
    
    def filter_by_time_range(self, start: datetime, end: datetime) -> List[DataPoint]:
        return [p for p in self.data_points 
                if start <= p.timestamp <= end]
    
    def filter_by_tags(self, tags: Dict[str, str]) -> List[DataPoint]:
        return [p for p in self.data_points 
                if all(p.tags.get(k) == v for k, v in tags.items())]

from influxdb_client import InfluxDBClient, Point, WriteOptions
from influxdb_client.client.write_api import SYNCHRONOUS
import datetime

class InfluxDBClient:
    def __init__(
        self,
        url: str = "http://localhost:8086",
        token: str = None,
        org: str = None
    ):
        self.client = InfluxDBClient(url=url, token=token, org=org)
        self.write_api = self.client.write_api(write_options=SYNCHRONOUS)
        self.query_api = self.client.query_api()
        self.delete_api = self.client.delete_api()
    
    def write_point(self, bucket: str, point: Point):
        return self.write_api.write(bucket=bucket, org=self.org, record=point)
    
    def write_points(self, bucket: str, points: List[Point]):
        return self.write_api.write(bucket=bucket, org=self.org, record=points)
    
    def write_line_protocol(self, bucket: str, line_protocol: str):
        return self.write_api.write(bucket=bucket, org=self.org, record=line_protocol)
    
    def query(self, query: str) -> List[dict]:
        return self.query_api.query_data_frame(query).to_dict('records')
    
    def close(self):
        self.client.close()

class InfluxDBWriter:
    def __init__(self, client: InfluxDBClient, bucket: str):
        self.client = client
        self.bucket = bucket
    
    def write_single_point(self, measurement: str, fields: dict, tags: dict = None, 
                           timestamp: datetime = None):
        point = Point(measurement)
        
        for key, value in (tags or {}).iteritems():
            point.tag(key, value)
        
        for key, value in fields.items():
            point.field(key, value)
        
        if timestamp:
            point.time(timestamp)
        
        self.client.write_point(self.bucket, point)
    
    def write_multiple_points(self, measurements: List[dict]):
        points = []
        
        for m in measurements:
            point = Point(m['measurement'])
            
            for key, value in m.get('tags', {}).items():
                point.tag(key, value)
            
            for key, value in m.get('fields', {}).items():
                point.field(key, value)
            
            if 'timestamp' in m:
                point.time(m['timestamp'])
            
            points.append(point)
        
        self.client.write_points(self.bucket, points)
    
    def write_with_write_options(self, bucket: str):
        write_api = self.client.client.write_api(
            write_options=WriteOptions(
                batch_size=1000,
                flush_interval=5000,
                jitter_interval=2000,
                retry_interval=5000,
                max_retries=5
            )
        )
        
        points = [Point("cpu").tag("host", "server01").field("value", i) 
                  for i in range(100)]
        
        write_api.write(bucket=bucket, org=self.client.org, record=points)

class InfluxDBQuerier:
    def __init__(self, client: InfluxDBClient):
        self.client = client
    
    def simple_query(self, measurement: str, start: str = "-1h"):
        query = f'''
        from(bucket: "my-bucket")
          |> range(start: {start})
          |> filter(fn: (r) => r._measurement == "{measurement}")
        '''
        return self.client.query(query)
    
    def query_with_fields(self, measurement: str, field: str, start: str = "-24h"):
        query = f'''
        from(bucket: "my-bucket")
          |> range(start: {start})
          |> filter(fn: (r) => r._measurement == "{measurement}")
          |> filter(fn: (r) => r._field == "{field}")
        '''
        return self.client.query(query)
    
    def query_with_tags(self, measurement: str, tag_filters: dict, start: str = "-1h"):
        filter_expr = ' and '.join(
            f'r["{key}"] == "{value}"' 
            for key, value in tag_filters.items()
        )
        
        query = f'''
        from(bucket: "my-bucket")
          |> range(start: {start})
          |> filter(fn: (r) => r._measurement == "{measurement}")
          |> filter(fn: (r) => {filter_expr})
        '''
        return self.client.query(query)
    
    def aggregate_query(self, measurement: str, window: str = "1m", 
                        function: str = "mean"):
        query = f'''
        from(bucket: "my-bucket")
          |> range(start: -1h)
          |> filter(fn: (r) => r._measurement == "{measurement}")
          |> aggregateWindow(every: {window}, fn: {function}, createEmpty: false)
        '''
        return self.client.query(query)
    
    def downsample_query(self, measurement: str, original_bucket: str, 
                         downsampled_bucket: str, window: str = "5m"):
        query = f'''
        from(bucket: "{original_bucket}")
          |> range(start: -30d)
          |> filter(fn: (r) => r._measurement == "{measurement}")
          |> aggregateWindow(every: {window}, fn: mean, createEmpty: false)
          |> to(bucket: "{downsampled_bucket}", org: "my-org")
        '''
        return self.client.query(query)

class InfluxDBSchema:
    @staticmethod
    def design_measurement(measurement_name: str, data_pattern: str) -> dict:
        if data_pattern == "high_cardinality":
            return {
                "measurement": measurement_name,
                "tags": [
                    "device_id",
                    "location"
                ],
                "fields": [
                    "value"
                ]
            }
        
        elif data_pattern == "low_cardinality":
            return {
                "measurement": measurement_name,
                "tags": [
                    "host",
                    "service",
                    "status"
                ],
                "fields": [
                    "cpu_percent",
                    "memory_percent",
                    "disk_percent"
                ]
            }
        
        return {}
    
    @staticmethod
    def get_tag_cardinality(client: InfluxDBClient, measurement: str) -> dict:
        query = f'''
        import "influxdata/influxdb/schema"
        schema.measurementTagValues(bucket: "my-bucket", measurement: "{measurement}")
        '''
        
        results = client.query(query)
        
        cardinality = {}
        for record in results:
            tag_key = record.get('tagKey', '')
            cardinality[tag_key] = cardinality.get(tag_key, 0) + 1
        
        return cardinality
    
    @staticmethod
    def optimize_schema(client: InfluxDBClient, measurement: str):
        cardinality = InfluxDBSchema.get_tag_cardinality(client, measurement)
        
        high_cardinality = {k: v for k, v in cardinality.items() if v > 10000}
        
        recommendations = []
        if high_cardinality:
            recommendations.append(
                f"Move {list(high_cardinality.keys())} to fields or bucket by time"
            )
        
        return recommendations

import psycopg2
from psycopg2.extras import execute_values
from datetime import datetime, timedelta
import time

class TimescaleDBConnection:
    def __init__(self, host: str, port: int, database: str, 
                 user: str, password: str):
        self.conn = psycopg2.connect(
            host=host,
            port=port,
            database=database,
            user=user,
            password=password
        )
        self.conn.autocommit = True
        self.cursor = self.conn.cursor()
    
    def execute(self, query: str, params: tuple = None):
        self.cursor.execute(query, params)
        return self.cursor
    
    def executemany(self, query: str, params_list: list):
        execute_values(self.cursor, query, params_list)
    
    def fetchall(self):
        return self.cursor.fetchall()
    
    def close(self):
        self.cursor.close()
        self.conn.close()

class TimescaleDBManager:
    def __init__(self, connection: TimescaleDBConnection):
        self.conn = connection
    
    def create_extension(self):
        self.conn.execute("CREATE EXTENSION IF NOT EXISTS timescaledb")
    
    def create_table(self, table_name: str):
        query = f'''
        CREATE TABLE IF NOT EXISTS {table_name} (
            time TIMESTAMPTZ NOT NULL,
            device_id TEXT,
            location TEXT,
            temperature DOUBLE PRECISION,
            humidity DOUBLE PRECISION,
            pressure DOUBLE PRECISION
        )
        '''
        self.conn.execute(query)
    
    def convert_to_hypertable(self, table_name: str, time_column: str = "time"):
        query = f'''
        SELECT create_hypertable(
            '{table_name}', 
            '{time_column}',
            if_not_exists => TRUE,
            migrate_data => TRUE
        )
        '''
        self.conn.execute(query)
    
    def create_hypertable_compressed(self, table_name: str, time_column: str = "time"):
        query = f'''
        SELECT create_hypertable(
            '{table_name}',
            '{time_column}',
            chunk_time_interval => INTERVAL '1 day',
            if_not_exists => TRUE
        )
        
        ALTER TABLE {table_name} SET (
            timescaledb.compress,
            timescaledb.compress_orderby = '{time_column} DESC',
            timescaledb.compress_segmentby = 'device_id, location'
        )
        '''
        self.conn.execute(query)
    
    def add_compression_policy(self, table_name: str, compress_after: str = "INTERVAL '7 days'"):
        query = f'''
        SELECT add_compression_policy(
            '{table_name}',
            {compress_after}
        )
        '''
        self.conn.execute(query)
    
    def create_continuous_aggregate(self, table_name: str, aggregate_name: str,
                                     bucket: str = "1 hour"):
        query = f'''
        CREATE MATERIALIZED VIEW {aggregate_name}
        WITH (timescaledb.continuous) AS
        SELECT 
            time_bucket('{bucket}', time) AS bucket,
            device_id,
            location,
            AVG(temperature) AS avg_temperature,
            AVG(humidity) AS avg_humidity,
            MAX(temperature) AS max_temperature,
            MIN(temperature) AS min_temperature,
            COUNT(*) AS sample_count
        FROM {table_name}
        GROUP BY bucket, device_id, location
        '''
        self.conn.execute(query)
    
    def add_refresh_policy(self, aggregate_name: str, start_offset: str = "INTERVAL '1 week'",
                          end_offset: str = "INTERVAL '1 hour'"):
        query = f'''
        SELECT add_continuous_aggregate_policy(
            '{aggregate_name}',
            start_offset => {start_offset},
            end_offset => {end_offset},
            schedule_interval => INTERVAL '1 hour'
        )
        '''
        self.conn.execute(query)

class TimescaleDBQuerier:
    def __init__(self, connection: TimescaleDBConnection):
        self.conn = connection
    
    def insert_data(self, table_name: str, data: list):
        query = f'''
        INSERT INTO {table_name} (time, device_id, location, temperature, humidity)
        VALUES %s
        '''
        self.conn.executemany(query, data)
    
    def insert_single(self, table_name: str, time: datetime, device_id: str,
                      location: str, temperature: float, humidity: float):
        query = f'''
        INSERT INTO {table_name} (time, device_id, location, temperature, humidity)
        VALUES (%s, %s, %s, %s, %s)
        '''
        self.conn.execute(query, (time, device_id, location, temperature, humidity))
    
    def query_range(self, table_name: str, start: datetime, end: datetime):
        query = f'''
        SELECT * FROM {table_name}
        WHERE time BETWEEN %s AND %s
        ORDER BY time DESC
        '''
        self.conn.execute(query, (start, end))
        return self.conn.fetchall()
    
    def query_device(self, table_name: str, device_id: str, start: datetime):
        query = f'''
        SELECT * FROM {table_name}
        WHERE device_id = %s AND time >= %s
        ORDER BY time DESC
        '''
        self.conn.execute(query, (device_id, start))
        return self.conn.fetchall()
    
    def time_bucket_query(self, table_name: str, bucket: str, start: datetime):
        query = f'''
        SELECT 
            time_bucket('{bucket}', time) AS bucket,
            AVG(temperature) AS avg_temp,
            MAX(temperature) AS max_temp,
            MIN(temperature) AS min_temp
        FROM {table_name}
        WHERE time >= %s
        GROUP BY bucket
        ORDER BY bucket
        '''
        self.conn.execute(query, (start,))
        return self.conn.fetchall()
    
    def last_value_query(self, table_name: str):
        query = f'''
        SELECT device_id, location, temperature, time
        FROM {table_name}
        WHERE time = (
            SELECT MAX(time) FROM {table_name} AS t2 
            WHERE t2.device_id = {table_name}.device_id
        )
        '''
        self.conn.execute(query)
        return self.conn.fetchall()
    
    def moving_average_query(self, table_name: str, window: str = "1 hour", start: datetime):
        query = f'''
        SELECT 
            time,
            temperature,
            AVG(temperature) OVER (
                ORDER BY time 
                ROWS BETWEEN {window} PRECEDING AND CURRENT ROW
            ) AS moving_avg
        FROM {table_name}
        WHERE time >= %s
        ORDER BY time
        '''
        self.conn.execute(query, (start,))
        return self.conn.fetchall()
    
    def gap_fill_query(self, table_name: str, start: datetime, bucket: str = "1 minute"):
        query = f'''
        SELECT 
            time_bucket('{bucket}', time) AS bucket,
            device_id,
            LOCF(avg(temperature)) AS temperature,
            INTERPOLATE(avg(humidity)) AS humidity
        FROM {table_name}
        WHERE time >= %s
        GROUP BY bucket, device_id
        ORDER BY bucket
        '''
        self.conn.execute(query, (start,))
        return self.conn.fetchall()

class TimescaleDBPerformance:
    def __init__(self, connection: TimescaleDBConnection):
        self.conn = connection
    
    def create_index(self, table_name: str, columns: list):
        index_name = f"idx_{table_name}_{'_'.join(columns)}"
        query = f'''
        CREATE INDEX IF NOT EXISTS {index_name} 
        ON {table_name} ({', '.join(columns)})
        '''
        self.conn.execute(query)
    
    def create_time_index(self, table_name: str, time_column: str = "time"):
        query = f'''
        CREATE INDEX IF NOT EXISTS idx_{table_name}_time 
        ON {table_name} ({time_column})
        '''
        self.conn.execute(query)
    
    def create_composite_index(self, table_name: str):
        query = f'''
        CREATE INDEX IF NOT EXISTS idx_{table_name}_composite
        ON {table_name} (device_id, time DESC)
        '''
        self.conn.execute(query)
    
    def get_chunk_info(self, table_name: str):
        query = f'''
        SELECT 
            chunk_schema,
            chunk_table,
            ranges
        FROM timescaledb_information.chunks
        WHERE hypertable_name = '{table_name}'
        '''
        self.conn.execute(query)
        return self.conn.fetchall()
    
    def get_compression_stats(self, table_name: str):
        query = f'''
        SELECT 
            chunk_table,
            before_compression_total_bytes,
            after_compression_total_bytes,
            before_compression_rows,
            after_compression_rows,
            compression_ratio
        FROM timescaledb_information.compressed_chunk_stats
        ORDER BY compression_ratio DESC
        '''
        self.conn.execute(query)
        return self.conn.fetchall()
    
    def reorder_chunk(self, table_name: str, older_than: str):
        query = f'''
        CALL reorder_chunk('{table_name}', older_than => {older_than})
        '''
        self.conn.execute(query)
    
    def drop_chunks(self, table_name: str, older_than: str):
        query = f'''
        SELECT drop_chunks('{table_name}', older_than => {older_than})
        '''
        self.conn.execute(query)

import threading
import queue
from typing import Callable

class TimeSeriesCollector:
    def __init__(self, batch_size: int = 100, flush_interval: float = 5.0):
        self.batch_size = batch_size
        self.flush_interval = flush_interval
        self.buffer = []
        self.buffer_lock = threading.Lock()
        self.running = False
    
    def add_data_point(self, measurement: str, fields: dict, 
                       tags: dict = None, timestamp: datetime = None):
        point = {
            'measurement': measurement,
            'fields': fields,
            'tags': tags or {},
            'timestamp': timestamp or datetime.utcnow()
        }
        
        with self.buffer_lock:
            self.buffer.append(point)
            
            if len(self.buffer) >= self.batch_size:
                return self._flush()
        
        return None
    
    def _flush(self) -> list:
        if not self.buffer:
            return None
        
        with self.buffer_lock:
            batch = self.buffer[:self.batch_size]
            self.buffer = self.buffer[self.batch_size:]
        
        return batch
    
    def start_flush_timer(self, flush_callback: Callable):
        def timer_loop():
            while self.running:
                time.sleep(self.flush_interval)
                batch = self._flush()
                if batch:
                    flush_callback(batch)
        
        self.running = True
        thread = threading.Thread(target=timer_loop, daemon=True)
        thread.start()
    
    def stop(self):
        self.running = False
        batch = self._flush()
        return batch

class MetricsAggregator:
    def __init__(self, db_client):
        self.client = db_client
    
    def aggregate_metrics(self, table_name: str, window: str = "1 minute"):
        queries = {
            "min": f'''
                SELECT 
                    time_bucket('{window}', time) AS bucket,
                    MIN(cpu) AS min_cpu,
                    MIN(memory) AS min_memory
                FROM {table_name}
                GROUP BY bucket
            ''',
            "max": f'''
                SELECT 
                    time_bucket('{window}', time) AS bucket,
                    MAX(cpu) AS max_cpu,
                    MAX(memory) AS max_memory
                FROM {table_name}
                GROUP BY bucket
            ''',
            "avg": f'''
                SELECT 
                    time_bucket('{window}', time) AS bucket,
                    AVG(cpu) AS avg_cpu,
                    AVG(memory) AS avg_memory
                FROM {table_name}
                GROUP BY bucket
            ''',
            "percentile": f'''
                SELECT 
                    time_bucket('{window}', time) AS bucket,
                    PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY cpu) AS p95_cpu,
                    PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY memory) AS p99_memory
                FROM {table_name}
                GROUP BY bucket
            '''
        }
        
        results = {}
        for name, query in queries.items():
            self.client.execute(query)
            results[name] = self.client.fetchall()
        
        return results
    
    def calculate_rate(self, table_name: str, counter_column: str):
        query = f'''
        SELECT 
            time,
            {counter_column},
            {counter_column} - LAG({counter_column}) OVER (ORDER BY time) AS rate
        FROM {table_name}
        WHERE time >= NOW() - INTERVAL '1 hour'
        '''
        self.client.execute(query)
        return self.client.fetchall()

class DataRetentionManager:
    def __init__(self, db_client, table_name: str):
        self.client = db_client
        self.table_name = table_name
    
    def create_retention_policy(self, keep_duration: str):
        if isinstance(self.client, TimescaleDBConnection):
            query = f'''
            SELECT add_retention_policy('{self.table_name}', INTERVAL '{keep_duration}')
            '''
            self.client.execute(query)
    
    def get_retention_info(self):
        if isinstance(self.client, TimescaleDBConnection):
            query = f'''
            SELECT * FROM timescaledb_information.retention_policies
            WHERE hypertable_name = '{self.table_name}'
            '''
            self.client.execute(query)
            return self.client.fetchall()
        return []
    
    def manual_cleanup(self, older_than: str):
        if isinstance(self.client, TimescaleDBConnection):
            query = f'''
            SELECT drop_chunks('{self.table_name}', older_than => INTERVAL '{older_than}')
            '''
            self.client.execute(query)
    
    def archive_old_data(self, archive_table: str, older_than: str):
        if isinstance(self.client, TimescaleDBConnection):
            query = f'''
            INSERT INTO {archive_table}
            SELECT * FROM {self.table_name}
            WHERE time < NOW() - INTERVAL '{older_than}'
            '''
            self.client.execute(query)
            
            self.manual_cleanup(older_than)

class TelegrafConfig:
    @staticmethod
    def generateInfluxDB_output(url: str, database: str, username: str, password: str) -> str:
        return f'''
[[outputs.influxdb]]
  urls = ["{url}"]
  database = "{database}"
  username = "{username}"
  password = "{password}"
  retention_policy = "autogen"
  write_consistency = "any"
  timeout = "5s"
'''

    @staticmethod
    def generateKafka_input(topic: str, brokers: list) -> str:
        brokers_str = ', '.join(f'"{b}"' for b in brokers)
        return f'''
[[inputs.kafka_consumer]]
  brokers = [{brokers_str}]
  topics = ["{topic}"]
  data_format = "influx"
'''

    @staticmethod
    def generateHTTPListener_input(port: int = 8186) -> str:
        return f'''
[[inputs.http_listener]]
  service_address = ":{port}"
  paths = ["/telegraf"]
  data_format = "influx"
'''

class PrometheusRemoteStorage:
    def __init__(self, influx_client: InfluxDBClient):
        self.client = influx_client
    
    def write_prometheus_remote_write(self, data: bytes):
        points = []
        
        for line in data.decode('utf-8').strip().split('\n'):
            if not line or line.startswith('#'):
                continue
            
            parts = line.split()
            metric_with_labels = parts[0]
            value = float(parts[1])
            
            if '{' in metric_with_labels:
                metric_name, labels_str = metric_with_labels.split('{')
                labels_str = labels_str.rstrip('}')
                labels = dict(
                    pair.split('=') for pair in labels_str.split(',')
                )
                labels = {k: v.strip('"') for k, v in labels.items()}
            else:
                metric_name = metric_with_labels
                labels = {}
            
            point = Point(metric_name).field("value", value)
            
            for key, val in labels.items():
                point.tag(key, val)
            
            points.append(point)
        
        if points:
            self.client.write_points("prometheus", points)
    
    def query_prometheus_range(self, query: str, start: str, end: str, step: str):
        flux_query = f'''
from(bucket: "prometheus")
  |> range(start: {start}, stop: {end})
  |> filter(fn: (r) => r._measurement == "{query}")
  |> aggregateWindow(every: {step}, fn: mean)
'''
        return self.client.query(flux_query)

class TimeSeriesMonitoring:
    def __init__(self, db_client):
        self.client = db_client
    
    def get_write_latency(self, table_name: str):
        query = f'''
        SELECT 
            time,
            latency
        FROM {table_name}
        WHERE metric = 'write_latency'
        AND time >= NOW() - INTERVAL '1 hour'
        '''
        self.client.execute(query)
        return self.client.fetchall()
    
    def get_query_performance(self, table_name: str):
        query = f'''
        SELECT 
            time_bucket('1 minute', time) AS bucket,
            COUNT(*) AS query_count,
            AVG(duration_ms) AS avg_duration,
            PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY duration_ms) AS p95_duration
        FROM {table_name}
        WHERE metric = 'query_duration'
        AND time >= NOW() - INTERVAL '24 hours'
        GROUP BY bucket
        ORDER BY bucket
        '''
        self.client.execute(query)
        return self.client.fetchall()
    
    def get_storage_usage(self, table_name: str):
        query = f'''
        SELECT 
            chunk_table,
            before_compression_total_bytes,
            after_compression_total_bytes
        FROM timescaledb_information.compressed_chunk_stats
        WHERE hypertable_name = '{table_name}'
        ORDER BY chunk_table DESC
        LIMIT 10
        '''
        self.client.execute(query)
        return self.client.fetchall()

TSDB_BEST_PRACTICES = {
    "schema_design": [
        "Use low-cardinality tags for filtering and grouping",
        "Use fields for high-cardinality values",
        "Keep tag keys consistent across measurements",
        "Avoid too many unique tag values (high cardinality)",
        "Use meaningful measurement names",
        "Consider partitioning by time and location"
    ],
    
    "data_ingestion": [
        "Batch writes for better throughput",
        "Use appropriate compression",
        "Set appropriate retention policies",
        "Monitor write latency and error rates",
        "Use bulk inserts when possible",
        "Configure appropriate batch sizes"
    ],
    
    "queries": [
        "Always filter by time range",
        "Use appropriate aggregation functions",
        "Leverage continuous aggregates for common queries",
        "Create indexes on frequently queried tags",
        "Use time bucketing for large datasets",
        "Consider pre-computed views"
    ],
    
    "retention": [
        "Define clear retention requirements",
        "Use automated compression policies",
        "Archive data before deletion",
        "Monitor storage growth",
        "Implement tiered storage",
        "Test restore procedures"
    ]
}

class TSDBPerformanceTuner:
    @staticmethod
    def tune_write_performance(batch_size: int = 1000, flush_interval: int = 5):
        return {
            "batch_size": batch_size,
            "flush_interval": flush_interval,
            "max_retries": 3,
            "use_compression": True
        }
    
    @staticmethod
    def tune_query_performance():
        return {
            "result_cache_size": "256MB",
            "max_cache_size": "1GB",
            "enable_parallel_queries": True,
            "enable_async_query": True
        }
    
    @staticmethod
    def tune_storage():
        return {
            "compression_enabled": True,
            "chunk_size": "7 days",
            "compress_after": "7 days",
            "index_on_time": True,
            "index_on_tags": True
        }