Bitquery GraphQL for Analytics on ClickHouse

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Altinity Ltd

SF Bay Area ClickHouse Meetup talk on integrating GraphQL with ClickHouse by Aleksey Studnev of Bitquery.io

Software

https://bitquery.io
Bitquery
GraphQL for Analytics
Aleksey Studnev

CTO, Founder

Agenda
Problem Formulation
Our Solution as GraphQL toolset
Demo of GraphQL toolset
Look Under the Hood
Analysis and Future Plans
bitquery.io

We Indexed Blockchain
bitquery.io
Blockchain

Network
ApplicationsClickhouse
ETL API
•FREE PUBLIC TIER

•SaaS ( SHARED ) SUBSCRIPTION TIER

•DEDICATED SYSTEM ON-PREMISES

API Layer Problems• Many Schemas, Tables, Clusters -> one API
➡ 40 blockchains
➡ 20 schemes
➡ 100 base tables + aggregates
• One API -> many clients, diversified requirements
➡ Easy to use HTTP / RPC / REST API
➡ Different requirements to fields / data joins
➡ SaaS model
• One API -> Many demands
➡ Flexible Data Parsing
➡ Optimisation / Aggregates
➡ Dictionaries
bitquery.io
Do not make me think!

Approaches That We Used / Considered
• SQL ( Superset, Redash )

• HTTP JSON API

• XMLA / MDX

• GraphQL
EASY TO USE
FLEXIBILITY / FEATURES
OUR DREAM

Graph QL Features
• It is JSON query language
• Schema defines «What you can query»
• You query only «What you need»
• Also Supports updates and subscriptions
• AFAIK, not used for OLAP ;)
bitquery.io

CUBE

NAME
DATASET

NAME
DIMENSIONSDIMENSIONSDIMENSIONS
METRICSMETRICSMETRICS
FILTERS

DIMENSION
bitquery.io
DIMENSION
ATTRIBUTE
FUNCTION

METRIC
bitquery.io
METRIC
ALIAS
FUNCTION

Graph QL for Analytics
• Easy to use toolset to build analytic queries
• Off-the-shelve tools for GraphQL editing, schema
composition, observablehq, Postman, etc..
• Basis for server-less analytical applications
• API - ready solution for SaaS clients
bitquery.io

Projects and Plans
• Build a community
• «FlexiGraph»: Schema = func ( Schema,… )
• Complexity Estimator
• Resources Measurement
• GraphQL IDE and Dashboard Builder
bitquery.io

https://bitquery.io
Thank You!
hello@bitquery.io

What's hot

This talk will start by explaining the optimal file format, compression algorithm, and file size for plain vanilla Parquet data lakes. It discusses the small file problem and how you can compact the small files. Then we will talk about partitioning Parquet data lakes on disk and how to examine Spark physical plans when running queries on a partitioned lake. We will discuss why it’s better to avoid PartitionFilters and directly grab partitions when querying partitioned lakes. We will explain why partitioned lakes tend to have a massive small file problem and why it’s hard to compact a partitioned lake. Then we’ll move on to Delta lakes and explain how they offer cool features on top of what’s available in Parquet. We’ll start with Delta 101 best practices and then move on to compacting with the OPTIMIZE command. We’ll talk about creating partitioned Delta lake and how OPTIMIZE works on a partitioned lake. Then we’ll talk about ZORDER indexes and how to incrementally update lakes with a ZORDER index. We’ll finish with a discussion on adding a ZORDER index to a partitioned Delta data lake.

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Nowadays, people are creating, sharing and storing data at a faster pace than ever before, effective data compression / decompression could significantly reduce the cost of data usage. Apache Spark is a general distributed computing engine for big data analytics, and it has large amount of data storing and shuffling across cluster in runtime, the data compression/decompression codecs can impact the end to end application performance in many ways. However, there’s a trade-off between the storage size and compression/decompression throughput (CPU computation). Balancing the data compress speed and ratio is a very interesting topic, particularly while both software algorithms and the CPU instruction set keep evolving. Apache Spark provides a very flexible compression codecs interface with default implementations like GZip, Snappy, LZ4, ZSTD etc. and Intel Big Data Technologies team also implemented more codecs based on latest Intel platform like ISA-L(igzip), LZ4-IPP, Zlib-IPP and ZSTD for Apache Spark; in this session, we’d like to compare the characteristics of those algorithms and implementations, by running different micro workloads as well as end to end workloads, based on different generations of Intel x86 platform and disk. It’s supposedly to be the best practice for big data software engineers to choose the proper compression/decompression codecs for their applications, and we also will present the methodologies of measuring and tuning the performance bottlenecks for typical Apache Spark workloads.

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Hive tables are an integral part of the big data ecosystem, but the simple directory-based design that made them ubiquitous is increasingly problematic. Netflix uses tables backed by S3 that, like other object stores, don’t fit this directory-based model: listings are much slower, renames are not atomic, and results are eventually consistent. Even tables in HDFS are problematic at scale, and reliable query behavior requires readers to acquire locks and wait. Owen O’Malley and Ryan Blue offer an overview of Iceberg, a new open source project that defines a new table layout addresses the challenges of current Hive tables, with properties specifically designed for cloud object stores, such as S3. Iceberg is an Apache-licensed open source project. It specifies the portable table format and standardizes many important features, including: * All reads use snapshot isolation without locking. * No directory listings are required for query planning. * Files can be added, removed, or replaced atomically. * Full schema evolution supports changes in the table over time. * Partitioning evolution enables changes to the physical layout without breaking existing queries. * Data files are stored as Avro, ORC, or Parquet. * Support for Spark, Pig, and Presto.

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Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner. In this talk, I am going examine a number common streaming design patterns in the context of the following questions. WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements? WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements? HOW are going to architect the solution? And how much are you willing to pay for it? Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.

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Bucketing is a partitioning technique that can improve performance in certain data transformations by avoiding data shuffling and sorting. The general idea of bucketing is to partition, and optionally sort, the data based on a subset of columns while it is written out (a one-time cost), while making successive reads of the data more performant for downstream jobs if the SQL operators can make use of this property. Bucketing can enable faster joins (i.e. single stage sort merge join), the ability to short circuit in FILTER operation if the file is pre-sorted over the column in a filter predicate, and it supports quick data sampling. In this session, you’ll learn how bucketing is implemented in both Hive and Spark. In particular, Patil will describe the changes in the Catalyst optimizer that enable these optimizations in Spark for various bucketing scenarios. Facebook’s performance tests have shown bucketing to improve Spark performance from 3-5x faster when the optimization is enabled. Many tables at Facebook are sorted and bucketed, and migrating these workloads to Spark have resulted in a 2-3x savings when compared to Hive. You’ll also hear about real-world applications of bucketing, like loading of cumulative tables with daily delta, and the characteristics that can help identify suitable candidate jobs that can benefit from bucketing.

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Data is a critical infrastructure for building machine learning systems. From ensuring accurate ETAs to predicting optimal traffic routes, providing safe, seamless transportation and delivery experiences on the Uber platform requires reliable, performant large-scale data storage and analysis. In 2016, Uber developed Apache Hudi, an incremental processing framework, to power business critical data pipelines at low latency and high efficiency, and helps distributed organizations build and manage petabyte-scale data lakes. In this talk, I will describe what is APache Hudi and its architectural design, and then deep dive to improving data operations by providing features such as data versioning, time travel. We will also go over how Hudi brings kappa architecture to big data systems and enables efficient incremental processing for near real time use cases. Speaker: Satish Kotha (Uber) Apache Hudi committer and Engineer at Uber. Previously, he worked on building real time distributed storage systems like Twitter MetricsDB and BlobStore. website: https://www.aicamp.ai/event/eventdetails/W2021043010

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Kubernetes As of Spark 2.3, Spark can run on clusters managed by Kubernetes. we will describes the best practices about running Spark SQL on Kubernetes upon Tencent cloud includes how to deploy Kubernetes against public cloud platform to maximum resource utilization and how to tune configurations of Spark to take advantage of Kubernetes resource manager to achieve best performance. To evaluate performance, the TPC-DS benchmarking tool will be used to analysis performance impact of queries between configurations set. Speakers: Junjie Chen, Junping Du

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Hudi architecture, fundamentals and capabilities

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Apache Hudi is a data lake platform, that provides streaming primitives (upserts/deletes/change streams) on top of data lake storage. Hudi powers very large data lakes at Uber, Robinhood and other companies, while being pre-installed on four major cloud platforms. Hudi supports exactly-once, near real-time data ingestion from Apache Kafka to cloud storage, which is typically used in-place of a S3/HDFS sink connector to gain transactions and mutability. While this approach is scalable and battle-tested, it can only ingest data in mini batches, leading to lower data freshness. In this talk, we introduce a Kafka Connect Sink Connector for Apache Hudi, which writes data straight into Hudi's log format, making the data immediately queryable, while Hudi's table services like indexing, compaction, clustering work behind the scenes, to further re-organize for better query performance.

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Few solutions exist in the open-source community either in the form of libraries or complete stand-alone platforms, which can be used to assure a certain data quality, especially when continuous imports happen. Organisations may consider picking up one of the available options – Apache Griffin, Deequ, DDQ and Great Expectations. In this presentation we’ll compare these different open-source products across different dimensions, like maturity, documentation, extensibility, features like data profiling and anomaly detection.

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Change Data Capture CDC is a typical use case in Real-Time Data Warehousing. It tracks the data change log -binlog- of a relational database [OLTP], and replay these change log timely to an external storage to do Real-Time OLAP, such as delta/kudu. To implement a robust CDC streaming pipeline, lots of factors should be concerned, such as how to ensure data accuracy , how to process OLTP source schema changed, whether it is easy to build for variety databases with less code.

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OSA Con 2022: Using ClickHouse Database to Power Analytics and Customer Engagement Platform Prafulla Gupta - Times Internet This talk covers how we empowered Product Managers and Editors at Times Internet by developing an in-house product, GrowthRx, using Clickhouse Open Source Database to track and analyze user behavior to increase user retention and customer engagement. Times Internet is India's largest digital news publisher, which manages leading brands like Times of India, Economic Times, Navbharat Times, etc, where we are tracking more than 10 billion events per month in the ClickHouse Database.

OSA Con 2022 - Using ClickHouse Database to Power Analytics and Customer Enga...

Altinity Ltd

OSA Con 2022 - Tips and Tricks to Keep Your Queries under 100ms with ClickHou...

Altinity Ltd

OSA Con 2022 - The Open Source Analytic Universe, Version 2022 - Robert Hodge...

Altinity Ltd

OSA Con 2022: Switching Jaeger Distributed Tracing to ClickHouse to Enable Advanced Performance Management Satbir Chahal - OpsVerse Our team switched our Jaeger (open source project used for distributed tracing) storage backend to ClickHouse (from Cassandra), which opened the door to a world of advanced analytics that we can run and provide our users. This talk will describe the journey from the switch, the learning curve, the challenges, and the eventual wins.

OSA Con 2022 - Switching Jaeger Distributed Tracing to ClickHouse to Enable A...

Altinity Ltd

OSA Con 2022 - Streaming Data Made Easy - Tim Spann & David Kjerrumgaard - St...

Altinity Ltd

OSA Con 2022 - State of Open Source Databases Peter Zaitsev - Percona It has been an exciting year in the open-source database industry, with more choices, more cloud, and key changes in the industry. We will dive into the key developments over 2022, including the most important open-source database software releases in general, the significance of cloud-native solutions in a multi-vendor multi-cloud world, the new criticality of security challenges, and the evolution of the open-source software industry.

OSA Con 2022 - State of Open Source Databases - Peter Zaitsev - Percona.pdf

Altinity Ltd

OSA Con 2022 - Specifics of data analysis in Time Series Databases - Roman Kh...

Altinity Ltd

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