Facebook, Inc.

Other

Zheng Shao

Samuel Rash

Dhruba Borthakur

Guanghao Shen

Prakash Khemani

Eric W Hwang

Dhrubajyoti Borthakur

Borthakur Dhrubajyoti

Eric Hwang

Techniques for facilitating and accelerating log data processing are disclosed herein. The front-end clusters generate a large amount of log data in real time and transfer the log data to an aggregating cluster. When the aggregating cluster is not available, the front-clusters write the log data to local filers and send the data when the aggregating cluster recovers. The aggregating cluster is designed to aggregate incoming log data streams from different front-end servers and clusters. The aggregating cluster further sends the aggregated log data stream to centralized NFS filers or a data warehouse cluster. The local filers and the aggregating cluster stage the log data for access by applications, so that the applications do not wait until the data reach the centralized NFS filers or data warehouse cluster.

Multi-level data staging for low latency data access

Techniques for facilitating and accelerating log data processing by splitting data streams are disclosed herein. The front-end clusters generate large amount of log data in real time and transfer the log data to an aggregating cluster. The aggregating cluster is designed to aggregate incoming log data streams from different front-end servers and clusters. The aggregating cluster further splits the log data into a plurality of data streams so that the data streams are sent to a receiving application in parallel. In one embodiment, the log data are randomly split to ensure the log data are evenly distributed in the split data streams. In another embodiment, the application that receives the split data streams determines how to split the log data.

Data stream splitting for low-latency data access

A method and system on failure recovery in a storage system are disclosed. In the storage system, user data streams (e.g., log data) are collected by a scribeh system. The scribeh system may include a plurality of Calligraphus servers, HDFS and Zookeeper. The Calligraphus servers may shard the user data streams based on keys (e.g., category and bucket pairs) and stream the user data streams to Puma nodes. Sharded user data streams may be aggregated according to the keys in memory of a specific Puma node. Periodically, aggregated user data streams cached in memory of the specific Puma node, together with a Incremental checkpoint, are persisted to HBase. When a specific process on the specific Puma node fails, Ptail retrieves the Incremental checkpoint from HBase and then restores the specific process by requesting user data streams processed by the specific process from the scribeh system according to the Incremental checkpoint.

Use of incremental checkpoints to restore user data stream processes

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Palo Alto, CA, United States of America

Zheng Shao