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[ci] apply automatic fixes
Signed-off-by: feldera-bot <feldera-bot@feldera.com>
1 parent 62f881d commit 9c54b2f

5 files changed

Lines changed: 16 additions & 7 deletions

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crates/adapters/src/controller.rs

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@@ -159,7 +159,8 @@ pub use feldera_types::config::{
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RuntimeConfig, TransportConfig,
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};
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use feldera_types::config::{
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DEFAULT_MAX_WORKER_BATCH_SIZE, DevTweaks, FileBackendConfig, FtConfig, FtModel, OutputBufferConfig, StorageBackendConfig, SyncConfig
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DEFAULT_MAX_WORKER_BATCH_SIZE, DevTweaks, FileBackendConfig, FtConfig, FtModel,
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OutputBufferConfig, StorageBackendConfig, SyncConfig,
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};
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use feldera_types::constants::{STATE_FILE, STEPS_FILE};
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use feldera_types::format::json::{JsonFlavor, JsonParserConfig, JsonUpdateFormat};

crates/buffer-cache/src/s3_fifo.rs

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@@ -1,6 +1,6 @@
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use crate::{BufferCache, CacheEntry};
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use feldera_types::config::dev_tweaks::BufferCacheStrategy;
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use quick_cache::{sync::Cache as QuickCache, OptionsBuilder, Weighter};
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use quick_cache::{OptionsBuilder, Weighter, sync::Cache as QuickCache};
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use std::any::Any;
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use std::hash::{BuildHasher, Hash, RandomState};
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crates/buffer-cache/src/tests/lru.rs

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@@ -1,5 +1,5 @@
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use crate::{CacheEntry, LruCache};
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use rand::{rngs::StdRng, Rng, SeedableRng};
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use rand::{Rng, SeedableRng, rngs::StdRng};
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use std::sync::Arc;
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use std::thread;
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crates/buffer-cache/src/tests/s3_fifo.rs

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@@ -1,5 +1,5 @@
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use crate::{CacheEntry, S3FifoCache};
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use rand::{rngs::StdRng, Rng, SeedableRng};
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use rand::{Rng, SeedableRng, rngs::StdRng};
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use std::sync::Arc;
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use std::thread;
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openapi.json

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@@ -8056,7 +8056,7 @@
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},
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"DevTweaks": {
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"type": "object",
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"description": "Optional settings for tweaking Feldera internals.",
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"description": "Optional settings for tweaking Feldera internals.\n\nThese settings reflect experiments that may come and go and change from\nversion to version. Users should not consider them to be stable.",
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"properties": {
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"adaptive_joins": {
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"type": "boolean",
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"balancer_min_absolute_improvement_threshold": {
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"type": "integer",
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"format": "int64",
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"description": "The minimum absolute improvement threshold for the balancer.\n\nThis parameter prevents the join balancer from making changes to the\npartitioning policy if the improvement is not significant, since the overhead\nof such rebalancing, especially when performed frequently, can exceed the benefits.\n\nA rebalancing is considered significant if the absolute estimated improvement for the cluster\nof joins where the rebalancing is applied is at least this threshold. The cost model used by\nthe balancer is based on the number of records in the largest partition of a collection.\n\nA rebalancing is applied if both this threshold and `balancer_min_relative_improvement_threshold` are met.\n\nThe default value is 10,000.",
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"description": "The minimum absolute improvement threshold for the balancer.\n\nThe join balancer is a component that dynamically chooses an optimal\npartitioning policy for adaptive join operators. This parameter\nprevents the join balancer from making changes to the partitioning\npolicy if the improvement is not significant, since the overhead of such\nrebalancing, especially when performed frequently, can exceed the\nbenefits.\n\nA rebalancing is considered significant if the absolute estimated\nimprovement for the cluster of joins where the rebalancing is applied is\nat least this threshold. The cost model used by the balancer is based on\nthe number of records in the largest partition of a collection.\n\nA rebalancing is applied if both this threshold and\n`balancer_min_relative_improvement_threshold` are met.\n\nThe default value is 10,000.",
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"default": null,
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"nullable": true,
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"minimum": 0
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},
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"balancer_min_relative_improvement_threshold": {
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"type": "number",
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"format": "double",
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"description": "The minimum relative improvement threshold for the join balancer.\n\nThis parameter prevents the join balancer from making changes to the\npartitioning policy if the improvement is not significant, since the overhead\nof such rebalancing, especially when performed frequently, can exceed the benefits.\n\nA rebalancing is considered significant if the relative estimated improvement for the cluster\nof joins where the rebalancing is applied is at least this threshold.\n\nA rebalancing is applied if both this threshold and `balancer_min_absolute_improvement_threshold` are met.\n\nThe default value is 1.2.",
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"description": "The minimum relative improvement threshold for the join balancer.\n\nThe join balancer is a component that dynamically chooses an optimal\npartitioning policy for adaptive join operators. This parameter\nprevents the join balancer from making changes to the partitioning\npolicy if the improvement is not significant, since the overhead of such\nrebalancing, especially when performed frequently, can exceed the\nbenefits.\n\nA rebalancing is considered significant if the relative estimated\nimprovement for the cluster of joins where the rebalancing is applied is\nat least this threshold.\n\nA rebalancing is applied if both this threshold and\n`balancer_min_absolute_improvement_threshold` are met.\n\nThe default value is 1.2.",
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"default": null,
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"nullable": true
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},
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"nullable": true,
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"minimum": 0
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},
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"negative_weight_multiplier": {
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"type": "integer",
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"format": "int32",
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"description": "Additional bias the merger assigns to records with negative weights\n(retractions) to promote them to higher levels of the LSM tree sooner.\n\nReasonable values for this parameter are in the range [0, 10].\n\nThe default value is 0, which means that retractions are not given\nany additional bias.",
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"default": null,
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"nullable": true,
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"minimum": 0
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},
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"splitter_chunk_size_records": {
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"type": "integer",
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"format": "int64",

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