This data warehousing use case is about . The user is , one of the world's biggest telecommunication service providers. Using Apache Doris, they deploy multiple petabyte-scale clusters on dozens of machines to support their 15 billion daily log additions from their over 30 business lines. Such a gigantic log analysis system is part of their cybersecurity management. For the need for real-time monitoring, threat tracing, and alerting, they require a log analytic system that can automatically collect, store, analyze, and visualize logs and event records. scale China Unicom From an architectural perspective, the system should be able to undertake real-time analysis of various formats of logs and, of course, be scalable to support the huge and ever-enlarging data size. The rest of this post is about what their log processing architecture looks like and how they realize stable data ingestion, low-cost storage, and quick queries with it. System Architecture This is an overview of their data pipeline. The logs are and go through several layers of processing. collected in the data warehouse : Original logs and alerts from all sources are gathered into Apache Kafka. Meanwhile, a copy of them will be stored in HDFS for data verification or replay. ODS : This is where the fact tables are. Apache Flink cleans, standardizes, backfills, and de-identifies the data and writes it back to Kafka. These fact tables will also be put into Apache Doris so that Doris can trace a certain item or use them for dashboarding and reporting. As logs are not averse to duplication, the fact tables will be arranged in the of Apache Doris. DWD Duplicate Key model : This layer aggregates data from DWD and lays the foundation for queries and analysis. DWS : In this layer, Apache Doris auto-aggregates data with its Aggregate Key model and auto-updates data with its Unique Key model. ADS Architecture 2.0 evolves from Architecture 1.0, which is supported by ClickHouse and Apache Hive. The transition arose from the user's needs for real-time and multi-table join queries. In their experience with ClickHouse, they found inadequate support for concurrency and multi-table joins, manifested by frequent timeouts in dashboarding and OOM errors in distributed joins. data processing Now, let's take a look at their practice in data ingestion, storage, and queries with Architecture 2.0. Real-Case Practice Stable ingestion of 15 billion logs per day In the user's case, their business churns out 15 billion logs every day. Ingesting such data volume quickly and stably is a real problem. With Apache Doris, the recommended way is to use the Flink-Doris-Connector. It was developed by the Apache Doris community for large-scale data writing. The component requires simple configuration. It implements Stream Load and can reach a writing speed of 200,000~300,000 logs per second without interrupting the data analytic workloads. A lesson learned is that when using Flink for high-frequency writing, you need to find the right parameter configuration for your case to avoid data version accumulation. In this case, the user made the following optimizations: : They increase the checkpoint interval from 15s to 60s to reduce writing frequency and the number of transactions processed by Doris per unit of time. This can relieve data writing pressure and avoid generating too many data versions. Flink Checkpoint : For data of the same ID but comes from various tables, Flink will pre-aggregate it based on the primary key ID and create a flat table in order to avoid excessive resource consumption caused by multi-source data writing. Data Pre-Aggregation : The trick here includes finding the right Doris backend (BE) parameters to allocate the right amount of CPU resources for data compaction, setting the appropriate number of data partitions, buckets, and replicas (too much data tablets will bring huge overheads), and dialing up to avoid version accumulation. Doris Compaction max_tablet_version_num These measures together ensure daily ingestion stability. The user has witnessed stable performance and low compaction scores in the Doris backend. In addition, the combination of data pre-processing in Flink and the in Doris can ensure quicker data updates. Unique Key model Storage strategies to reduce costs by 50% The size and generation rate of logs also impose pressure on storage. Among the immense log data, only a part of it is of high informational value, so storage should be differentiated. The user has three storage strategies to reduce costs. : For tables larger than 1TB, specify the compression method as "ZSTD." Upon table creation, it will realize a compression ratio of 10:1. ZSTD (ZStandard) compression algorithm : This is supported by the of Doris. The user sets a data "cooldown" period of 7 days. That means data from the past seven days (namely, hot data) will be stored in SSD. As time goes by, hot data "cools down" (getting older than seven days), and it will be automatically moved to HDD, which is less expensive. As data gets even "colder,” it will be moved to object storage for much lower storage costs. Plus, in object storage, data will be stored with only one copy instead of three. This further cuts down costs and the overheads brought by redundant storage. Tiered storage of hot and cold data new feature : The user has partitioned their data by time range. The principle is to have more replicas for newer data partitions and less for the older ones. In their case, data from the past 3 months is frequently accessed, so they have 2 replicas for this partition. Data that is 3~6 months old has two replicas, and data from 6 months ago has one single copy. Differentiated replica numbers for different data partitions With these three strategies, the user has reduced their storage costs by 50%. Differentiated query strategies based on data size Some logs must be immediately traced and located, such as those of abnormal events or failures. To ensure real-time response to these queries, the user has different query strategies for different data sizes: : The user utilizes the dynamic partitioning feature of Doris. Small tables will be partitioned by date, and large tables will be partitioned by hour. This can avoid data skew. To further ensure the balance of data within a partition, they use the snowflake ID as the bucketing field. They also set a starting offset of 20 days, which means data from the recent 20 days will be kept. In this way, they find the balance point between data backlog and analytic needs. Less than 100G : These tables have their materialized views, which are the pre-computed result sets stored in Doris. Thus, queries on these tables will be much faster and less resource-consuming. The DDL syntax of materialized views in Doris is the same as those in PostgreSQL and Oracle. 100G~1T : These tables are put into the Aggregate Key model of Apache Doris and pre-aggregate them. More than 100T In this way, we enable queries of 2 billion log records to be done in 1~2s. These strategies have shortened the response time of queries. For example, a query of a specific data item used to take minutes, but now it can be finished in milliseconds. In addition, for big tables that contain 10 billion , queries on different dimensions can all be done in a few seconds. data records Ongoing Plans The user is now testing with the newly added in Apache Doris. It is designed to speed up full-text search of strings as well as equivalence and range queries of numerics and datetime. They have also provided valuable feedback about the auto-bucketing logic in Doris: Currently, Doris decides the number of buckets for a partition  based on the data size of the previous partition. The problem for the user is most of their new data comes in during the daytime but little at night. So, in their case, Doris creates too many buckets for night data but too few in daylight, which is the opposite of what they need. They hope to add a new auto-bucketing logic, where the reference for Doris to decide the number of buckets is the data size and distribution of the previous day. They've come to the Apache Doris community, and we are now working on this optimization. inverted index Also published . here

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