Answering billions of reporting queries every day with low latency – Google Analysis Weblog

Google Ads infrastructure runs on an inner knowledge warehouse known as Napa. Billions of reporting queries, which energy essential dashboards utilized by promoting purchasers to measure marketing campaign efficiency, run on tables saved in Napa. These tables comprise data of advertisements efficiency which can be keyed utilizing explicit prospects and the marketing campaign identifiers with which they’re related. Keys are tokens which can be used each to affiliate an advertisements report with a specific shopper and marketing campaign (e.g., customer_id, campaign_id) and for environment friendly retrieval. A report incorporates dozens of keys, so purchasers use reporting queries to specify keys wanted to filter the info to know advertisements efficiency (e.g., by area, gadget and metrics corresponding to clicks, and so forth.). What makes this drawback difficult is that the info is skewed since queries require various ranges of effort to be answered and have stringent latency expectations. Particularly, some queries require the usage of thousands and thousands of data whereas others are answered with just some.

To this finish, in “Progressive Partitioning for Parallelized Query Execution in Napa”, offered at VLDB 2023, we describe how the Napa knowledge warehouse determines the quantity of machine assets wanted to reply reporting queries whereas assembly strict latency targets. We introduce a brand new progressive question partitioning algorithm that may parallelize question execution within the presence of complicated knowledge skews to carry out persistently effectively in a matter of some milliseconds. Lastly, we exhibit how Napa permits Google Advertisements infrastructure to serve billions of queries day by day.

Question processing challenges

When a shopper inputs a reporting question, the primary problem is to find out easy methods to parallelize the question successfully. Napa’s parallelization approach breaks up the question into even sections which can be equally distributed throughout obtainable machines, which then course of these in parallel to considerably scale back question latency. That is completed by estimating the variety of data related to a specified key, and assigning roughly equal quantities of labor to machines. Nonetheless, this estimation will not be good since reviewing all data would require the identical effort as answering the question. A machine that processes considerably greater than others would lead to run-time skews and poor efficiency. Every machine additionally must have ample work since unnecessary parallelism results in underutilized infrastructure. Lastly, parallelization must be a per question choice that should be executed near-perfectly billions of occasions, or the question could miss the stringent latency necessities.

The reporting question instance under extracts the data denoted by keys (i.e., customer_id and campaign_id) after which computes an mixture (i.e., SUM(price)) from an advertiser desk. On this instance the variety of data is just too massive to course of on a single machine, so Napa wants to make use of a subsequent key (e.g., adgroup_id) to additional break up the gathering of data in order that equal distribution of labor is achieved. You will need to observe that at petabyte scale, the scale of the info statistics wanted for parallelization could also be a number of terabytes. Which means the issue isn’t just about gathering huge quantities of metadata, but in addition how it’s managed.

        SELECT customer_id, campaign_id, SUM(price)
             FROM advertiser_table
             WHERE customer_id in (1, 7, ..., x )
             AND campaign_id in (10, 20, ..., y)
             GROUP BY customer_id, campaign_id;

This reporting question instance extracts data denoted by keys (i.e., customer_id and campaign_id) after which computes an mixture (i.e., SUM(price)) from an advertiser desk. The question effort is decided by the keys’ included within the question. Keys belonging to purchasers with bigger campaigns could contact thousands and thousands of data because the knowledge quantity immediately correlates with the scale of the advertisements marketing campaign. This disparity of matching data based mostly on keys displays the skewness in knowledge, which makes question processing a difficult drawback.

An efficient resolution minimizes the quantity of metadata wanted, focuses effort totally on the skewed a part of the important thing house to partition knowledge effectively, and works effectively throughout the allotted time. For instance, if the question latency is just a few hundred milliseconds, partitioning ought to take now not than tens of milliseconds. Lastly, a parallelization course of ought to decide when it is reached the absolute best partitioning that considers question latency expectations. To this finish, now we have developed a progressive partitioning algorithm that we describe later on this article.

Managing the info deluge

Tables in Napa are continually up to date, so we use log-structured merge forests (LSM tree) to arrange the deluge of desk updates. LSM is a forest of sorted knowledge that’s temporally organized with a B-tree index to assist environment friendly key lookup queries. B-trees retailer abstract data of the sub-trees in a hierarchical method. Every B-tree node data the variety of entries current in every subtree, which aids within the parallelization of queries. LSM permits us to decouple the method of updating the tables from the mechanics of question serving within the sense that dwell queries go towards a distinct model of the info, which is atomically up to date as soon as the following batch of ingest (known as delta) has been absolutely ready for querying.

The partitioning drawback

The info partitioning drawback in our context is that now we have a massively massive desk that’s represented as an LSM tree. Within the determine under, Delta 1 and a pair of every have their very own B-tree, and collectively signify 70 data. Napa breaks the data into two items, and assigns every bit to a distinct machine. The issue turns into a partitioning drawback of a forest of bushes and requires a tree-traversal algorithm that may shortly cut up the bushes into two equal components.

To keep away from visiting all of the nodes of the tree, we introduce the idea of “adequate” partitioning. As we start reducing and partitioning the tree into two components, we keep an estimate of how dangerous our present reply can be if we terminated the partitioning course of at that immediate. That is the yardstick of how shut we’re to the reply and is represented under by a complete error margin of 40 (at this level of execution, the 2 items are anticipated to be between 15 and 35 data in measurement, the uncertainty provides as much as 40). Every subsequent traversal step reduces the error estimate, and if the 2 items are roughly equal, it stops the partitioning course of. This course of continues till the specified error margin is reached, at which period we’re assured that the 2 items are roughly equal.

Progressive partitioning algorithm

Progressive partitioning encapsulates the notion of “adequate” in that it makes a collection of strikes to scale back the error estimate. The enter is a set of B-trees and the aim is to chop the bushes into items of roughly equal measurement. The algorithm traverses one of many bushes (“drill down” within the determine) which leads to a discount of the error estimate. The algorithm is guided by statistics which can be saved with every node of the tree in order that it makes an knowledgeable set of strikes at every step. The problem right here is to resolve easy methods to direct effort in the absolute best approach in order that the error sure reduces shortly within the fewest attainable steps. Progressive partitioning is conducive for our use-case because the longer the algorithm runs, the extra equal the items develop into. It additionally implies that if the algorithm is stopped at any level, one nonetheless will get good partitioning, the place the standard corresponds to the time spent.

Prior work on this house makes use of a sampled table to drive the partitioning process, whereas the Napa strategy makes use of a B-tree. As talked about earlier, even only a pattern from a petabyte desk may be large. A tree-based partitioning methodology can obtain partitioning far more effectively than a sample-based strategy, which doesn’t use a tree group of the sampled data. We examine progressive partitioning with an alternate strategy, the place sampling of the desk at numerous resolutions (e.g., 1 report pattern each 250 MB and so forth) aids the partitioning of the question. Experimental outcomes present the relative speedup from progressive partitioning for queries requiring various numbers of machines. These outcomes exhibit that progressive partitioning is far sooner than present approaches and the speedup will increase as the scale of the question will increase.


Napa’s progressive partitioning algorithm effectively optimizes database queries, enabling Google Advertisements to serve shopper reporting queries billions of occasions every day. We observe that tree traversal is a typical approach that college students in introductory pc science programs use, but it additionally serves a essential use-case at Google. We hope that this text will encourage our readers, because it demonstrates how easy strategies and thoroughly designed knowledge buildings may be remarkably potent if used effectively. Try the paper and a recent talk describing Napa to be taught extra.


This weblog publish describes a collaborative effort between Junichi Tatemura, Tao Zou, Jagan Sankaranarayanan, Yanlai Huang, Jim Chen, Yupu Zhang, Kevin Lai, Hao Zhang, Gokul Nath Babu Manoharan, Goetz Graefe, Divyakant Agrawal, Brad Adelberg, Shilpa Kolhar and Indrajit Roy.

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