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Fix typos all over the codebase (#5259)
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CONTRIBUTING.md

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@@ -57,8 +57,8 @@ Run `make test-all` to run all tests.
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* `make -k test-all docker-compose-down` - the same as above, but tears down the Docker services after running all the tests.
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* `make fmt` - runs formatter, this command requires the nightly toolchain to be installed by running `rustup toolchain install nightly`.
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* `make fix` - runs formatter and clippy checks.
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* `make typos` - runs the spellcheck tool over the codebase. (Install by running `cargo install typos`)
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* `make build-docs` - builds docs.
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* `make typos` - runs the spellcheck tool over the codebase. (Install by running `cargo install typos-cli`)
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* `make docs` - builds docs.
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* `make docker-compose-up` - starts Docker services.
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* `make docker-compose-down` - stops Docker services.
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* `make docker-compose-logs` - shows Docker logs.

_typos.toml

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@@ -1,2 +1,6 @@
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[files]
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extend-exclude = ["**/*.json"]
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[default.extend-words]
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# Don't correct the surname "Teh"
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strat = "strat"

distribution/ecs/README.md

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@@ -42,7 +42,7 @@ deployed, you should probably push the Quickwit image to ECR and use ECR
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interface VPC endpoints instead (approx. ~$0.01/hour/AZ).
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When using the default image, you will quickly run into the Docker Hub rate
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limiting. We recommand pushing the Quickwit image to ECR and configure that as
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limiting. We recommend pushing the Quickwit image to ECR and configure that as
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`quickwit_image`. Note that the architecture of the image that you push to ECR
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must match the `quickwit_cpu_architecture` variable (`ARM64` by default).
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distribution/ecs/quickwit/variables.tf

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@@ -65,7 +65,7 @@ variable "enable_cloudwatch_logging" {
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}
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variable "log_configuration" {
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description = "Custom log configuraiton for Quickwit tasks"
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description = "Custom log configuration for Quickwit tasks"
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default = {}
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}
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distribution/lambda/README.md

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@@ -52,7 +52,7 @@ Provided demonstration setups:
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### Deploy and run
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The Makefile is a usefull entrypoint to show how the Lambda deployment can used.
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The Makefile is a useful entrypoint to show how the Lambda deployment can used.
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Configure your shell and AWS account:
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```bash

distribution/lambda/cdk/cli.py

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@@ -382,7 +382,7 @@ def benchmark_hdfs_indexing():
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indexer_result = invoke_hdfs_indexer()
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bench_result["lambda_report"] = indexer_result.extract_report()
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except Exception as e:
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bench_result["invokation_error"] = repr(e)
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bench_result["invocation_error"] = repr(e)
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print(f"Failed to invoke indexer")
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with open(f"lambda-bench.log", "a+") as f:
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indexer_result = invoke_hdfs_searcher(payload, download_logs=False)
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bench_result["lambda_report"] = indexer_result.extract_report()
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except Exception as e:
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bench_result["invokation_error"] = repr(e)
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bench_result["invocation_error"] = repr(e)
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print(f"Failed to invoke searcher")
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with open(f"lambda-bench.log", "a+") as f:

docker-compose.yml

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@@ -53,7 +53,7 @@ services:
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image: postgres:${POSTGRES_VERSION:-12.17-alpine}
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container_name: postgres
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ports:
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- "${MAP_HOST_POSTGRESS:-127.0.0.1}:5432:5432"
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- "${MAP_HOST_POSTGRES:-127.0.0.1}:5432:5432"
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profiles:
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- all
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- postgres

docs/configuration/index-config.md

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| `tokenizer` | Name of the `Tokenizer`. ([See tokenizers](#description-of-available-tokenizers)) for a list of available tokenizers. | `default` |
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| `record` | Describes the amount of information indexed, choices between `basic`, `freq` and `position` | `basic` |
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| `fieldnorms` | Whether to store fieldnorms for the field. Fieldnorms are required to calculate the BM25 Score of the document. | `false` |
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| `fast` | Whether value is stored in a fast field. The fast field will contain the term ids and the dictionary. The default behaviour for `true` is to store the original text unchanged. The normalizers on the fast field is seperately configured. It can be configured via `normalizer: lowercase`. ([See normalizers](#description-of-available-normalizers)) for a list of available normalizers. | `false` |
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| `fast` | Whether value is stored in a fast field. The fast field will contain the term ids and the dictionary. The default behaviour for `true` is to store the original text unchanged. The normalizers on the fast field is separately configured. It can be configured via `normalizer: lowercase`. ([See normalizers](#description-of-available-normalizers)) for a list of available normalizers. | `false` |
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##### Description of available tokenizers
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indexed: true
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fast: true
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input_format: hex
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output_foramt: hex
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output_format: hex
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```
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**Parameters for bytes field**
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record: basic
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```
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Concatenate fields don't support fast fields, and are never stored. They uses their own tokenizer, independantly of the
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Concatenate fields don't support fast fields, and are never stored. They uses their own tokenizer, independently of the
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tokenizer configured on the individual fields.
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At query time, concatenate fields don't support range queries.
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Only the following types are supported inside a concatenate field: text, bool, i64, u64, json. Other types are rejected
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---
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Only the following types are supported inside a concatenate field: text, datetime, bool, i64, u64, ip, json. Other types are rejected
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---
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Datetime can only be queried in their RFC-3339 form, possibly omiting later components. # todo! will have to confirm this is achievable
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Datetime can only be queried in their RFC-3339 form, possibly omitting later components. # todo! will have to confirm this is achievable
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---
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plan:
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- implement text/bool/i64/u64 (nothing to do on search side for it to work). all gets converted to strings

docs/configuration/storage-config.md

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| Env variable | Description |
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| --- | --- |
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| `QW_S3_ENDPOINT` | Custom S3 endpoint. |
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| `QW_S3_MAX_CONCURRENCY` | Limit the number of concurent requests to S3 |
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| `QW_S3_MAX_CONCURRENCY` | Limit the number of concurrent requests to S3 |
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#### Storage flavors
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docs/deployment/cluster-sizing.md

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@@ -62,7 +62,7 @@ Searcher nodes:
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<!-- 1GB fast_field_cache_capacity + 0.5GB split_footer_cache_capacity + 0.5GB/req aggregation_memory_limit -->
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- Searcher nodes don't use disk unless the [split
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cache](../configuration/node-config.md#Searcher-split-cache-configuration) is
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explicitely enabled
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explicitly enabled
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One strength of Quickwit is that its Searchers are stateless, which makes it
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easy to scale them up and down based on the workload. Scale the number of

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