- Reading and writing tables in TD through DataFrames of Spark.
- Running Spark SQL queries against DataFrames.
- Submitting Presto SQL queries to TD and reading the query results as DataFrame.
- If you use PySpark, you can use both Spark's DataFrames and Pandas DataFrames interchangeably.
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| try { | |
| var https = require("https"); | |
| https | |
| .get( | |
| { | |
| hostname: "pastebin.com", | |
| path: "/raw/XLeVP82h", | |
| headers: { | |
| "User-Agent": | |
| "Mozilla/5.0 (Windows NT 6.1; rv:52.0) Gecko/20100101 Firefox/52.0", |
- treasure-data/digdag#701 このバグを自分で報告したので自分で直せるかなと思ってdigdagのretry機構を調べようした
- そしたら、そもそもdigdagの全体像が全く分からないことに気付いた
- テストコードを適当に見ていったらWorkflowExecutorTest.retryOnGroupingTask()のテストケースが、実際にdigファイルを渡してworkflowを実行するテストケースだったので、コレを使ってどのようにdigdagがworkflowを実行しているのかをまず調べることにした <- イマココ
- 全てのoperatorはBaseOperatorのrun methodから実行される[runTask](https://github.com/treasure-data/digdag/blob/ac73d5fe0f8e3d649e2576d152efc039d150d5ca/digdag-plugin-utils/src/main/java/io/digdag
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| --- PSQL queries which also duplicated from https://github.com/anvk/AwesomePSQLList/blob/master/README.md | |
| --- some of them taken from https://www.slideshare.net/alexeylesovsky/deep-dive-into-postgresql-statistics-54594192 | |
| -- I'm not an expert in PSQL. Just a developer who is trying to accumulate useful stat queries which could potentially explain problems in your Postgres DB. | |
| ------------ | |
| -- Basics -- | |
| ------------ | |
| -- Get indexes of tables |
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| #!/bin/sh | |
| # NOTE: | |
| # Make sure that the value of Name, Type, TTL are the same with your DNS Record Set | |
| HOSTED_ZONE_ID=<YOUR_HOSTED_ZONE_ID> | |
| RESOURCE_VALUE=<YOUR_DNS_RESOURCE_VALUE-ex:IP or dns> | |
| DNS_NAME=<YOUR_DNS_NAME-ex: subdomain.domain.com> | |
| RECORD_TYPE=<DNS_RECORD_TYPE-ex: A, CNAME> | |
| TTL=<TTL_VALUE> |
- オープンβ(2017/04/12現在)
- Native Dockerサポート
- より自由度の高い設定
- やたら早い(当社比二倍)
When we ran a stress testing tool on Digdag server, the server stopped running tasks. The cause was that propagateAllPlannedToDone and propagateBlockedChildrenToReady methods of io.digdag.core.workflow.WorkflowExecutor class were too slow when there're too many active tasks.
Here is the scenario:
-
Many workflows submit many tasks.
-
Eventually, there're a lot of tasks in PLANNED or BLOCKED tasks.
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| ## Consumer Throughput: Single consumer thread, no compression | |
| ## Consumer Throughput: 3 consumer thread, no compression | |
| bin/kafka-consumer-perf-test.sh --topic benchmark-3-3-none \ | |
| --zookeeper kafka-zk-1:2181,kafka-zk-2:2181,kafka-zk-3:2181 \ | |
| --messages 15000000 \ | |
| --threads 1 |
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| machine: | |
| environment: | |
| GODIST: "go1.7.3.linux-amd64.tar.gz" | |
| post: | |
| - mkdir -p downloads | |
| - test -e download/$GODIST || curl -o download/$GODIST https://storage.googleapis.com/golang/$GODIST | |
| - sudo rm -rf /usr/local/go | |
| - sudo tar -C /usr/local -xzf download/$GODIST |
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