EventQL from 10,000 feetA quick introduction to the EventQL architecture.
Cloudera Blog
Riding the wave of the generative AI revolution, third party large language model (LLM) services like ChatGPT and Bard have swiftly emerged as the talk of the town, converting AI skeptics to evangelists and transforming the way we interact with technology. For proof of this megatrend look no further than the instant success of ChatGPT, […] Read blog post
PostgreSQL9.3をカラム指向ストレージ(cstore_fdw)に対応させる
分析向けデータベースを展開している CitusDB が PostgreSQL を列指向ストレージ対応させる foreign data wrapper(cstore_fdw) をオープンソース化したので、とりあえずインストールしてみた。 cstore_fdw の特徴 github の cstore_fdw に特徴がまとめられている。 http://citusdata.github.io/cstore_fdw/ 箇条書きすると Faster Analytics – Reduce analytics query disk and memory use by 10x Lower Storage – Compress data by 3x Easy Setup – Deploy as standard PostgreSQL extension Flexibility – Mix row- and c
A tour through hybrid column/row-oriented DBMS schemes
There has been a lot of talk recently about hybrid column-store/row-store database systems. This is likely due to many announcements along these lines in the past month, such as Vertica’s recent 3.5 release which contained FlexStore, Oracle’s recent revelation that Oracle Database 11g Release 2 uses column-oriented storage for the purposes of superior compression, and VectoreWise’s recent decloaki
Who is How Columnar? Exadata, Teradata, and HANA – Part 1: Column Compression
There are three forms of columnar-orientation currently deployed by database systems today. Each builds upon the next. The simplest form uses column-orientation to provide better data compression. The next level of maturity stores columnar data in separate structures to support columnar projection. The most mature implementations support a columnar database engine that performs relational algebra
A BILLION ROWS PER SECOND Metaprogramming Python for Big Data
Ville Tuulos Principal Engineer @ AdRoll ville.tuulos@adroll.com We faced the key technical challenge of modern Business Intelligence: How to query tens of billions of events interactively? Our solution, DeliRoll, is implemented in Python. Everyone knows that Python is SLOW. You can't handle big data with low latency in Python! Small Benchmark Data: 1.5 billion rows, 400 columns - 660GB. Smaller e
Metaprogramming Python for Big Data
For many companies, understanding what is going on in your business involves lots of data. But, how do you query 10s of billions of data points? How can a company begin to make sense of so much information? Ville Tuulos, Principle Engineer at AdRoll, a company producing tons of big data, demonstrates how AdRoll uses Python to squeeze every bit of performance out of a single high-end server. They m
Hadoop Hive - ORC Files
ORC File Format File Structure Stripe Structure HiveQLSyntax Serialization and Compression Integer Column Serialization String Column Serialization Compression ORC File Format The Optimized Row Columnar (ORC) file format provides a highly efficient way to store Hive data. It was designed to overcome limitations of the other Hive file formats. Using ORC files improves performance when Hive is readi
Byte-dictionary encoding - Amazon Redshift
In byte dictionary encoding, a separate dictionary of unique values is created for each block of column values on disk. (An Amazon Redshift disk block occupies 1 MB.) The dictionary contains up to 256 one-byte values that are stored as indexes to the original data values. If more than 256 values are stored in a single block, the extra values are written into the block in raw, uncompressed form. Th
Text255 and Text32k encodings - Amazon Redshift
Text255 and text32k encodings are useful for compressing VARCHAR columns in which the same words recur often. A separate dictionary of unique words is created for each block of column values on disk. (An Amazon Redshift disk block occupies 1 MB.) The dictionary contains the first 245 unique words in the column. Those words are replaced on disk by a one-byte index value representing one of the 245
Mostly encoding - Amazon Redshift
Mostly encodings are useful when the data type for a column is larger than most of the stored values require. By specifying a mostly encoding for this type of column, you can compress the majority of the values in the column to a smaller standard storage size. The remaining values that cannot be compressed are stored in their raw form. For example, you can compress a 16-bit column, such as an INT2
Apache HBase I/O - HFile - Cloudera Blog
Introduction Apache HBase is the Hadoop open-source, distributed, versioned storage manager well suited for random, realtime read/write access. Wait wait? random, realtime read/write access? How is that possible? Is not Hadoop just a sequential read/write, batch processing system? Yes, we’re talking about the same thing, and in the next few paragraphs, I’m going to explain to you how HBase achiev
Delta encoding - Amazon Redshift
Delta encodings are very useful for date time columns. Delta encoding compresses data by recording the difference between values that follow each other in the column. This difference is recorded in a separate dictionary for each block of column values on disk. (An Amazon Redshift disk block occupies 1 MB.) For example, suppose that the column contains 10 integers in sequence from 1 to 10. The firs
グーグルのBigQuery、高速処理の仕組みは「カラム型データストア」と「ツリー構造」。解説文書が公開 － Publickey
グーグルのBigQuery、高速処理の仕組みは「カラム型データストア」と「ツリー構造」。解説文書が公開 SQLのクエリに対応し、3億件を超えるデータに対してインデックスを使わないフルスキャン検索で10秒以内に結果を出す。グーグルのBigQueryは大規模なクエリを超高速で実行する能力を提供するサービスです。その内部を解説する文書「An Inside Look at Google BigQuery」（PDF）を公開しました。 グーグルは大規模クエリを実行するサービスとして社内でコードネーム「Dremel」を構築しており、2010年にそのDremelを解説する文書「Dremel: Interactive Analysis of Web-Scale Datasets」を公開しています。BigQueryは、そのDremelを外部公開向けに実装したものです。 グーグルはこのDremel/BigQue
Dremel made simple with Parquet
Columnar storage is a popular technique to optimize analytical workloads in parallel RDBMs. The performance and compression benefits for storing and processing large amounts of data are well documented in academic literature as well as several commercial analytical databases. The goal is to keep I/O to a minimum by reading from a disk only the data required for the query. Using Parquet at Twitter,
Cloudera Blog
Riding the wave of the generative AI revolution, third party large language model (LLM) services like ChatGPT and Bard have swiftly emerged as the talk of the town, converting AI skeptics to evangelists and transforming the way we interact with technology. For proof of this megatrend look no further than the instant success of ChatGPT, […] Read blog post
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Efficient Data Storage for Analytics with Parquet 2.0 - Hadoop Summit 2014
Parquet - Data I/O - Philadelphia 2013
GitHub - metamx/druid: Real²time Exploratory Analytics on Large Datasets
Parquet Hadoop Summit 2013
