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Revert "(perf) move s3 logging to Batch logging + async [94% faster p… (#6275)
* Revert "(perf) move s3 logging to Batch logging + async [94% faster perf under 100 RPS on 1 litellm instance] (#6165)"
This reverts commit 2a5624af47.
* fix test s3
* add test_basic_s3_logging
This commit is contained in:
Ishaan Jaff
GitHub
parent
81766e7049
commit
dd4f01a75e
8 changed files with 171 additions and 343 deletions
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@ -1,67 +1,43 @@
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"""
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s3 Bucket Logging Integration
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#### What this does ####
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# On success + failure, log events to Supabase
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async_log_success_event: Processes the event, stores it in memory for 10 seconds or until MAX_BATCH_SIZE and then flushes to s3
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NOTE 1: S3 does not provide a BATCH PUT API endpoint, so we create tasks to upload each element individually
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NOTE 2: We create a httpx client with a concurrent limit of 1 to upload to s3. Files should get uploaded BUT they should not impact latency of LLM calling logic
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"""
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import asyncio
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import json
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from datetime import datetime
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from typing import Dict, List, Optional
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import datetime
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import os
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import subprocess
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import sys
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import traceback
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import uuid
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from typing import Optional
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import litellm
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from litellm._logging import print_verbose, verbose_logger
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from litellm.llms.base_aws_llm import BaseAWSLLM
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from litellm.llms.custom_httpx.http_handler import (
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_get_httpx_client,
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get_async_httpx_client,
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httpxSpecialProvider,
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)
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from litellm.types.integrations.s3 import s3BatchLoggingElement
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from litellm.types.utils import StandardLoggingPayload
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from .custom_batch_logger import CustomBatchLogger
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# Default Flush interval and batch size for s3
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# Flush to s3 every 10 seconds OR every 1K requests in memory
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DEFAULT_S3_FLUSH_INTERVAL_SECONDS = 10
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DEFAULT_S3_BATCH_SIZE = 1000
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class S3Logger(CustomBatchLogger, BaseAWSLLM):
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class S3Logger:
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# Class variables or attributes
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def __init__(
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self,
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s3_bucket_name: Optional[str] = None,
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s3_path: Optional[str] = None,
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s3_region_name: Optional[str] = None,
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s3_api_version: Optional[str] = None,
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s3_use_ssl: bool = True,
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s3_verify: Optional[bool] = None,
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s3_endpoint_url: Optional[str] = None,
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s3_aws_access_key_id: Optional[str] = None,
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s3_aws_secret_access_key: Optional[str] = None,
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s3_aws_session_token: Optional[str] = None,
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s3_flush_interval: Optional[int] = DEFAULT_S3_FLUSH_INTERVAL_SECONDS,
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s3_batch_size: Optional[int] = DEFAULT_S3_BATCH_SIZE,
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s3_bucket_name=None,
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s3_path=None,
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s3_region_name=None,
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s3_api_version=None,
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s3_use_ssl=True,
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s3_verify=None,
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s3_endpoint_url=None,
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s3_aws_access_key_id=None,
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s3_aws_secret_access_key=None,
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s3_aws_session_token=None,
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s3_config=None,
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**kwargs,
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):
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import boto3
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try:
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verbose_logger.debug(
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f"in init s3 logger - s3_callback_params {litellm.s3_callback_params}"
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)
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# IMPORTANT: We use a concurrent limit of 1 to upload to s3
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# Files should get uploaded BUT they should not impact latency of LLM calling logic
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self.async_httpx_client = get_async_httpx_client(
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llm_provider=httpxSpecialProvider.LoggingCallback,
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params={"concurrent_limit": 1},
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)
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if litellm.s3_callback_params is not None:
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# read in .env variables - example os.environ/AWS_BUCKET_NAME
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for key, value in litellm.s3_callback_params.items():
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@ -87,282 +63,107 @@ class S3Logger(CustomBatchLogger, BaseAWSLLM):
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s3_path = litellm.s3_callback_params.get("s3_path")
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# done reading litellm.s3_callback_params
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s3_flush_interval = litellm.s3_callback_params.get(
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"s3_flush_interval", DEFAULT_S3_FLUSH_INTERVAL_SECONDS
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)
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s3_batch_size = litellm.s3_callback_params.get(
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"s3_batch_size", DEFAULT_S3_BATCH_SIZE
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)
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self.bucket_name = s3_bucket_name
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self.s3_path = s3_path
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verbose_logger.debug(f"s3 logger using endpoint url {s3_endpoint_url}")
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self.s3_bucket_name = s3_bucket_name
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self.s3_region_name = s3_region_name
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self.s3_api_version = s3_api_version
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self.s3_use_ssl = s3_use_ssl
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self.s3_verify = s3_verify
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self.s3_endpoint_url = s3_endpoint_url
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self.s3_aws_access_key_id = s3_aws_access_key_id
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self.s3_aws_secret_access_key = s3_aws_secret_access_key
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self.s3_aws_session_token = s3_aws_session_token
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self.s3_config = s3_config
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self.init_kwargs = kwargs
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asyncio.create_task(self.periodic_flush())
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self.flush_lock = asyncio.Lock()
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verbose_logger.debug(
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f"s3 flush interval: {s3_flush_interval}, s3 batch size: {s3_batch_size}"
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# Create an S3 client with custom endpoint URL
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self.s3_client = boto3.client(
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"s3",
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region_name=s3_region_name,
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endpoint_url=s3_endpoint_url,
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api_version=s3_api_version,
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use_ssl=s3_use_ssl,
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verify=s3_verify,
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aws_access_key_id=s3_aws_access_key_id,
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aws_secret_access_key=s3_aws_secret_access_key,
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aws_session_token=s3_aws_session_token,
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config=s3_config,
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**kwargs,
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)
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# Call CustomLogger's __init__
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CustomBatchLogger.__init__(
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self,
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flush_lock=self.flush_lock,
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flush_interval=s3_flush_interval,
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batch_size=s3_batch_size,
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)
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self.log_queue: List[s3BatchLoggingElement] = []
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# Call BaseAWSLLM's __init__
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BaseAWSLLM.__init__(self)
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except Exception as e:
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print_verbose(f"Got exception on init s3 client {str(e)}")
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raise e
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async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
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async def _async_log_event(
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self, kwargs, response_obj, start_time, end_time, print_verbose
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):
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self.log_event(kwargs, response_obj, start_time, end_time, print_verbose)
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def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
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try:
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verbose_logger.debug(
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f"s3 Logging - Enters logging function for model {kwargs}"
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)
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s3_batch_logging_element = self.create_s3_batch_logging_element(
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start_time=start_time,
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standard_logging_payload=kwargs.get("standard_logging_object", None),
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s3_path=self.s3_path,
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# construct payload to send to s3
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# follows the same params as langfuse.py
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litellm_params = kwargs.get("litellm_params", {})
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metadata = (
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litellm_params.get("metadata", {}) or {}
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) # if litellm_params['metadata'] == None
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# Clean Metadata before logging - never log raw metadata
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# the raw metadata can contain circular references which leads to infinite recursion
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# we clean out all extra litellm metadata params before logging
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clean_metadata = {}
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if isinstance(metadata, dict):
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for key, value in metadata.items():
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# clean litellm metadata before logging
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if key in [
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"headers",
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"endpoint",
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"caching_groups",
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"previous_models",
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]:
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continue
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else:
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clean_metadata[key] = value
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# Ensure everything in the payload is converted to str
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payload: Optional[StandardLoggingPayload] = kwargs.get(
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"standard_logging_object", None
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)
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if s3_batch_logging_element is None:
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raise ValueError("s3_batch_logging_element is None")
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if payload is None:
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return
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verbose_logger.debug(
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"\ns3 Logger - Logging payload = %s", s3_batch_logging_element
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s3_file_name = litellm.utils.get_logging_id(start_time, payload) or ""
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s3_object_key = (
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(self.s3_path.rstrip("/") + "/" if self.s3_path else "")
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+ start_time.strftime("%Y-%m-%d")
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+ "/"
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+ s3_file_name
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) # we need the s3 key to include the time, so we log cache hits too
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s3_object_key += ".json"
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s3_object_download_filename = (
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"time-"
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+ start_time.strftime("%Y-%m-%dT%H-%M-%S-%f")
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+ "_"
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+ payload["id"]
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+ ".json"
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)
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self.log_queue.append(s3_batch_logging_element)
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verbose_logger.debug(
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"s3 logging: queue length %s, batch size %s",
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len(self.log_queue),
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self.batch_size,
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import json
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payload_str = json.dumps(payload)
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print_verbose(f"\ns3 Logger - Logging payload = {payload_str}")
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response = self.s3_client.put_object(
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Bucket=self.bucket_name,
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Key=s3_object_key,
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Body=payload_str,
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ContentType="application/json",
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ContentLanguage="en",
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ContentDisposition=f'inline; filename="{s3_object_download_filename}"',
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CacheControl="private, immutable, max-age=31536000, s-maxage=0",
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)
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if len(self.log_queue) >= self.batch_size:
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await self.flush_queue()
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print_verbose(f"Response from s3:{str(response)}")
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print_verbose(f"s3 Layer Logging - final response object: {response_obj}")
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return response
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except Exception as e:
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verbose_logger.exception(f"s3 Layer Error - {str(e)}")
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pass
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def log_success_event(self, kwargs, response_obj, start_time, end_time):
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"""
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Synchronous logging function to log to s3
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Does not batch logging requests, instantly logs on s3 Bucket
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"""
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try:
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s3_batch_logging_element = self.create_s3_batch_logging_element(
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start_time=start_time,
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standard_logging_payload=kwargs.get("standard_logging_object", None),
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s3_path=self.s3_path,
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)
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if s3_batch_logging_element is None:
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raise ValueError("s3_batch_logging_element is None")
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verbose_logger.debug(
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"\ns3 Logger - Logging payload = %s", s3_batch_logging_element
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)
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# log the element sync httpx client
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self.upload_data_to_s3(s3_batch_logging_element)
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except Exception as e:
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verbose_logger.exception(f"s3 Layer Error - {str(e)}")
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pass
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async def async_upload_data_to_s3(
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self, batch_logging_element: s3BatchLoggingElement
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):
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try:
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import hashlib
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import boto3
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import requests
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from botocore.auth import SigV4Auth
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from botocore.awsrequest import AWSRequest
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from botocore.credentials import Credentials
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except ImportError:
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raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
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try:
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credentials: Credentials = self.get_credentials(
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aws_access_key_id=self.s3_aws_access_key_id,
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aws_secret_access_key=self.s3_aws_secret_access_key,
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aws_session_token=self.s3_aws_session_token,
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aws_region_name=self.s3_region_name,
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)
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# Prepare the URL
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url = f"https://{self.bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}"
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if self.s3_endpoint_url:
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url = self.s3_endpoint_url + "/" + batch_logging_element.s3_object_key
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# Convert JSON to string
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json_string = json.dumps(batch_logging_element.payload)
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# Calculate SHA256 hash of the content
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content_hash = hashlib.sha256(json_string.encode("utf-8")).hexdigest()
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# Prepare the request
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headers = {
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"Content-Type": "application/json",
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"x-amz-content-sha256": content_hash,
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"Content-Language": "en",
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"Content-Disposition": f'inline; filename="{batch_logging_element.s3_object_download_filename}"',
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"Cache-Control": "private, immutable, max-age=31536000, s-maxage=0",
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}
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req = requests.Request("PUT", url, data=json_string, headers=headers)
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prepped = req.prepare()
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# Sign the request
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aws_request = AWSRequest(
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method=prepped.method,
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url=prepped.url,
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data=prepped.body,
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headers=prepped.headers,
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)
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SigV4Auth(credentials, "s3", self.s3_region_name).add_auth(aws_request)
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# Prepare the signed headers
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signed_headers = dict(aws_request.headers.items())
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# Make the request
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response = await self.async_httpx_client.put(
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url, data=json_string, headers=signed_headers
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)
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response.raise_for_status()
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except Exception as e:
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verbose_logger.exception(f"Error uploading to s3: {str(e)}")
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async def async_send_batch(self):
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"""
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Sends runs from self.log_queue
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Returns: None
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Raises: Does not raise an exception, will only verbose_logger.exception()
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"""
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if not self.log_queue:
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return
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for payload in self.log_queue:
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asyncio.create_task(self.async_upload_data_to_s3(payload))
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def create_s3_batch_logging_element(
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self,
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start_time: datetime,
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standard_logging_payload: Optional[StandardLoggingPayload],
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s3_path: Optional[str],
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) -> Optional[s3BatchLoggingElement]:
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"""
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Helper function to create an s3BatchLoggingElement.
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Args:
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start_time (datetime): The start time of the logging event.
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standard_logging_payload (Optional[StandardLoggingPayload]): The payload to be logged.
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s3_path (Optional[str]): The S3 path prefix.
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Returns:
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Optional[s3BatchLoggingElement]: The created s3BatchLoggingElement, or None if payload is None.
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"""
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if standard_logging_payload is None:
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return None
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s3_file_name = (
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litellm.utils.get_logging_id(start_time, standard_logging_payload) or ""
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)
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s3_object_key = (
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(s3_path.rstrip("/") + "/" if s3_path else "")
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+ start_time.strftime("%Y-%m-%d")
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+ "/"
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+ s3_file_name
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+ ".json"
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)
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s3_object_download_filename = f"time-{start_time.strftime('%Y-%m-%dT%H-%M-%S-%f')}_{standard_logging_payload['id']}.json"
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return s3BatchLoggingElement(
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payload=standard_logging_payload, # type: ignore
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s3_object_key=s3_object_key,
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s3_object_download_filename=s3_object_download_filename,
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)
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def upload_data_to_s3(self, batch_logging_element: s3BatchLoggingElement):
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try:
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import hashlib
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|
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import boto3
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import requests
|
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from botocore.auth import SigV4Auth
|
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from botocore.awsrequest import AWSRequest
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from botocore.credentials import Credentials
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except ImportError:
|
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raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
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try:
|
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credentials: Credentials = self.get_credentials(
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aws_access_key_id=self.s3_aws_access_key_id,
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aws_secret_access_key=self.s3_aws_secret_access_key,
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aws_session_token=self.s3_aws_session_token,
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aws_region_name=self.s3_region_name,
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)
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|
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# Prepare the URL
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url = f"https://{self.bucket_name}.s3.{self.s3_region_name}.amazonaws.com/{batch_logging_element.s3_object_key}"
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|
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if self.s3_endpoint_url:
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url = self.s3_endpoint_url + "/" + batch_logging_element.s3_object_key
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|
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# Convert JSON to string
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json_string = json.dumps(batch_logging_element.payload)
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|
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# Calculate SHA256 hash of the content
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content_hash = hashlib.sha256(json_string.encode("utf-8")).hexdigest()
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|
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# Prepare the request
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headers = {
|
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"Content-Type": "application/json",
|
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"x-amz-content-sha256": content_hash,
|
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"Content-Language": "en",
|
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"Content-Disposition": f'inline; filename="{batch_logging_element.s3_object_download_filename}"',
|
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"Cache-Control": "private, immutable, max-age=31536000, s-maxage=0",
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}
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req = requests.Request("PUT", url, data=json_string, headers=headers)
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prepped = req.prepare()
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|
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# Sign the request
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aws_request = AWSRequest(
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method=prepped.method,
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url=prepped.url,
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data=prepped.body,
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headers=prepped.headers,
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||||
)
|
||||
SigV4Auth(credentials, "s3", self.s3_region_name).add_auth(aws_request)
|
||||
|
||||
# Prepare the signed headers
|
||||
signed_headers = dict(aws_request.headers.items())
|
||||
|
||||
httpx_client = _get_httpx_client()
|
||||
# Make the request
|
||||
response = httpx_client.put(url, data=json_string, headers=signed_headers)
|
||||
response.raise_for_status()
|
||||
except Exception as e:
|
||||
verbose_logger.exception(f"Error uploading to s3: {str(e)}")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue