* use t.Fatal(f) to abort tests, not panic * make perf_able run at all, make it debug a bit better switch perf-able to using same node type we use for other spot instances, because otherwise it never finds any available capacity. we switch the perf-able script to use the standard get_value function instead of direct jq calls. we try to grab server logs if the restore fails in the hopes of finding out why the restore very occasionally fails. * Fix some issues with running IDK tests in docker. (#2248) *Stop running TestKafkaSourceIntegration with t.Parallel() This test can't be run in parallel as it's currently written. Doing so allows for interleaving of messages to the same kafka topic between tests. I didn't attempt to modify the test so it could be run in parallel. That could be done, but left for someone more ambitious. * Remove idk/testenv/certs which got accidentally committed. also update .gitignore to include those. * changes to add bool support in idk (#2240) * initial changes to add bool support in idk * modifying some default parameters for testing, will revert them later * adding support for bool in making fragments function * boolean values implementation without supporting empty or null values at this point * Implement bool support in batch using a map (and a slice for nulls) (#2247) * Implement bool support in batch using a map (and a slice for nulls) * Keep the PackBools default for now But set it explicity in the ingest tests which rely on it. * Modify batch to construct bool update like mutex The code in API.ImportRoaringShard has a switch statement which causes bool fields to be handled like mutex fields. This means, that the viewUpdate.Clear value should only contain data in the first "row" of the fragment, which it will treat as records to clear for *all* rows. This makes more sense for mutex fields; for bool fields, there's only one other row to clear. But since the code is currently handling them the same, we need to construct viewUpdate.Clear such that it conforms to that pattern. This commit also adds a test which covers this logic. * Remove commented code; revert config for testing This commit also removes the DELETE_SENTINEL case for non-packed bools, since that isn't supported anyway. * Revert default setting * remove inconsistent type scope * correcting the logic of string converstion to bool * resolving an error in a test * adding tests to cover code related to bool support in batch.go file and interface.go files * modifying interfaces test * added one more test case Co-authored-by: Travis Turner <travis@pilosa.com> Co-authored-by: Travis Turner <travis@molecula.com> * resolving bool null field ingestion error (#2254) * resolving bool null field ingestion error * testing issues * adding null support for bools * updating the null bool field ingestion * trying to resolve issue when ingesting null value for bool type * adding a clearing support for bool type * resolving issues with bool null value ingestion * updating the jwt go package version and removing changes made in docker compose file * reverting jwt go version * removing v4 of jwt * adding a comment in test file to see if sonar cloud accepts this file * don't obtain stack traces on rbf.Tx creation We thought stack traces were mildly expensive. We were very wrong. Due to a complicated issue in the Go runtime, simultaneous requests for stack traces end up contending on a lock even when they're not actually contending on any resources. I've filed a ticket in the Go issue tracker for this: https://github.com/golang/go/issues/56400 In the mean time: Under some workloads, we were seeing 85% of all CPU time go into the stack backtraces, of which 81% went into the contention on those locks. But even if you take away the contention, that leaves us with 4/19 of all CPU time in our code going into building those stack backtraces. That's a lot of overhead for a feature we virtually never use. We might consider adding a backtrace functionality here, possibly using `runtime.Callers` which is much lower overhead, and allows us to generate a backtrace on demand (no argument values available, but then, we never read those because they're unformatted hex values), but I don't think it's actually very informative to know what the stack traces were of the Tx; they don't necessarily reflect the current state of any ongoing use of the Tx, so we can't necessarily correlate them to goroutine stack dumps, and so on. * fb-1729 Enriched Table Metadata (#2255) enriched metadata for tables added support for the concept of a table and field owners in metadata; mechanism to derive owner from http request metadata; metadata for table description * tightened up is/is not null filter expressions (FB-1741) (#2260) Covers tightening up handling filter expressions that contain is/is not null ops. These filters may have to be translated into PQL calls to be passed to the executor and even though sql3 language supports nullability for any data type, currently only BSI fields are nullable at the storage engine level (there is a ticket to add support for non-BSI field here FB-1689: IS SQL Argument returns incorrect error) so when these fields are used in filter conditions we need to handle BSI and non-BSI fields differently. * added a test to cover the keyword replace as being synonymous with insert (#2261) * update molecula references to featurebase (#2262) Co-authored-by: Seebs <seebs@molecula.com> Co-authored-by: Travis Turner <travis@pilosa.com> Co-authored-by: Pranitha-malae <56414132+Pranitha-malae@users.noreply.github.com> Co-authored-by: Travis Turner <travis@molecula.com> Co-authored-by: pokeeffe-molecula <85502298+pokeeffe-molecula@users.noreply.github.com> Co-authored-by: Stephanie Yang <stephanie@pilosa.com> |
||
|---|---|---|
| .. | ||
| api | ||
| bankgen | ||
| cmd | ||
| common | ||
| csv | ||
| datagen | ||
| docker-sasl | ||
| fakeidp | ||
| idktest | ||
| internal | ||
| kafka | ||
| kafka_sasl | ||
| kafka_static | ||
| kafkagen | ||
| kinesis | ||
| sql | ||
| testdata | ||
| .cloud-env.template | ||
| docker-compose.yml | ||
| Dockerfile | ||
| Dockerfile-fakeIDP | ||
| Dockerfile-test | ||
| Dockerfile-wait | ||
| dup.go | ||
| dup_arm64.go | ||
| file_with_line_delimited_values | ||
| header.go | ||
| header_test.go | ||
| idallocator.go | ||
| idallocator_test.go | ||
| ingest.go | ||
| ingest_test.go | ||
| interfaces.go | ||
| interfaces_test.go | ||
| Makefile | ||
| metrics.go | ||
| pilosa-sec-test.conf | ||
| postgres.go | ||
| progress.go | ||
| README.md | ||
| reingest_test.sh | ||
| sample.csv | ||
| test_postgres.go | ||
| tls.go | ||
| util.go | ||
| util_test.go | ||
| version.go | ||
| wait.sh | ||
idk: Ingest Development Kit
Integration tests
To run the tests, you will need to install the following dependencies:
In addition to these dependancies, you will need to be added to the moleculacorp Dockerhub account.
First start the test environment. This is a docker-compose environment that includes pilosa and a confluent kafka stack. Run the following to start those services:
BRANCH_NAME=master make startup
To build and run the integration tests, run:
make test-run
Then to shut down the test environment, run:
make shutdown
You can run all of the previous commands by calling test-all:
BRANCH_NAME=master make test-all
The previous command is equivalent to running the following:
make startup
sleep 30 # wait for services to come up
make test-run
make shutdown
To run an individual test, you can run the command directly using docker-compose. Note that you must run docker-compose build idk-test for docker to run the latest code. Modify the following as needed:
make startup
docker-compose build idk-test
docker-compose run idk-test /usr/local/go/bin/go test -count=1 -mod=vendor -run=TestCmdMainOne ./kafka
To shutdown and reset the environment:
make clean
Running dependencies locally (rather than in docker) "make test-local"
This is for running the tests locally and not in Docker... so you have to be running a bunch of stuff natively on your machine.
Run Pilosa with default config:
pilosa server
Run another pilosa like
pilosa server --config=pilosa-sec-test.conf
which will run Pilosa with TLS using certs in testenv. (make testenv first if you haven't).
You also need to be running the Confluent stack which, after you've installed it from Confluent's site might look something like:
export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.8.0_66.jdk/Contents/Home
confluent local destroy && confluent local start schema-registry
Or it might not! You may not need the first line, but if you have the wrong Java version by default, that's how you set it. The second line may change depending on what version of the confluent stack you get. According to
confluent version
I'm running:
Version: v0.212.0
Git Ref: 2b04985
Use the test-local make target:
make test-local
This sets a number of environment variables (which it prints when you run it), and should set them correctly if you follow the instructions above, but if you're running things on non-default ports you may need to tweak them.
CSV Ingester
- make sure you're running Pilosa (localhost:10101 for these instructions)
molecula-consumer-csv --primary-key-fields=asset_tag -i sample-index --files sample.csv
asset_tag__String,fan_time__RecordTime_2006-01-02,fan_val__String_F_YMD
ABCD,2019-01-02,70%
ABCD,2019-01-03,20%
ABCD,2019-01-04,30%
BEDF,2019-01-02,70%
BEDF,2019-01-05,90%
BEDF,2019-01-08,10%
BEDF,2019-01-08,20%
ABCD,2019-01-30,40%
Datagen
Datagen is an internal command-line tool to generate various application-specific datasets, and ingest them directly into Pilosa. After running make install, run datagen with no arguments to see a list of available "sources".
When running Datagen with local Docker stacks, make sure to add individual docker stacks to the /etc/hosts file:
127.0.0.1 kafka
127.0.0.1 pilosa
127.0.0.1 <docker stack>
ODBC Support
By default, the SQL ingester is not built with ODBC support.
This is because it uses CGO with extra dependencies, and the resulting binaries are not portable.
In order to build with ODBC support, it is necessary to install unixODBC to the system.
Then run:
make bin/molecula-consumer-sql-odbc
Different Linux distros will store certain libraries in different locations. ODBC uses dynamic library loading to handle drivers, so full static linking of dependencies is not possible. It is therefore necessary to build on a system with the same distro and the same software versions as the target machine.