Looking for Datadog logos? You can find the logo assets on our press page.
In this post I want to compare ClickHouse, Druid, and Pinot, the three open source data stores that run analytical queries over big volumes of data with interactive latencies. Warning: this post is pretty big, you may want to read just the “Summary” section in the end. Sources of InformationI learned the implementation details of ClickHouse from Alexey Zatelepin, one of the core developers. The be
In a prior blog post, I introduced the basics of stateful processing in Apache Beam, focusing on the addition of state to per-element processing. So-called timely processing complements stateful processing in Beam by letting you set timers to request a (stateful) callback at some point in the future. What can you do with timers in Beam? Here are some examples: You can output data buffered in state
Beam lets you process unbounded, out-of-order, global-scale data with portable high-level pipelines. Stateful processing is a new feature of the Beam model that expands the capabilities of Beam, unlocking new use cases and new efficiencies. In this post, I will guide you through stateful processing in Beam: how it works, how it fits in with the other features of the Beam model, what you might use
Window functions: Something like this should do the trick: import org.apache.spark.sql.functions.{row_number, max, broadcast} import org.apache.spark.sql.expressions.Window val df = sc.parallelize(Seq( (0,"cat26",30.9), (0,"cat13",22.1), (0,"cat95",19.6), (0,"cat105",1.3), (1,"cat67",28.5), (1,"cat4",26.8), (1,"cat13",12.6), (1,"cat23",5.3), (2,"cat56",39.6), (2,"cat40",29.7), (2,"cat187",27.9), (
Foundations of streaming SQL or: how I learned to love stream & table theory Tyler Akidau Apache Beam PMC Software Engineer at Google @takidau Covering ideas from across the Apache Beam, Apache Calcite, Apache Kafka, and Apache Flink communities, with thoughts and contributions from Julian Hyde, ...
Should You Put Several Event Types in the Same Kafka Topic? If you adopt a streaming platform such as Apache Kafka, one of the most important questions to answer is: what topics are you going to use? In particular, if you have a bunch of different events that you want to publish to Kafka as messages, do you put them in the same topic, or do you split them across different topics? The most importan
In our Monitoring 101 series, we introduced a high-level framework for monitoring and alerting on metrics and events from your applications and infrastructure. In this series we’ll go a bit deeper on alerting specifics, breaking down several different alert types. In this post we cover four types of status checks that poll or ping a particular component to verify if it is up or down: Host checksSe
Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoop’s flexibility by utilizing standard componen
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く