mirror of
https://github.com/lancedb/lancedb.git
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feat: add analyze_plan api (#2280)
add analyze plan api to allow executing the queries and see runtime metrics. Which help identify the query IO overhead and help identify query slowness
This commit is contained in:
LuQQiu
GitHub
101
Cargo.lock
generated
101
Cargo.lock
generated
@@ -390,9 +390,9 @@ dependencies = [
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[[package]]
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name = "async-compression"
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version = "0.4.21"
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version = "0.4.22"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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dependencies = [
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"flate2",
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"futures-core",
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@@ -512,9 +512,9 @@ checksum = "ace50bade8e6234aa140d9a2f552bbee1db4d353f69b8217bc503490fc1a9f26"
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[[package]]
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name = "aws-config"
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version = "1.6.0"
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version = "1.6.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "6a84fe2c5e9965fba0fbc2001db252f1d57527d82a905cca85127df227bca748"
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checksum = "8c39646d1a6b51240a1a23bb57ea4eebede7e16fbc237fdc876980233dcecb4f"
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dependencies = [
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"aws-credential-types",
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"aws-runtime",
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@@ -603,9 +603,9 @@ dependencies = [
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[[package]]
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name = "aws-sdk-bedrockruntime"
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version = "1.77.0"
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version = "1.78.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "4198493316dab97e1fed7716f3823462b73a34c518f4ee7b9799921645e232e5"
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checksum = "6457fd617f20075dd2d5c9f6c3cb09815e6b83ab61c55499055499da9e04478d"
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dependencies = [
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"aws-credential-types",
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"aws-runtime",
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@@ -627,9 +627,9 @@ dependencies = [
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[[package]]
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name = "aws-sdk-dynamodb"
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version = "1.69.0"
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version = "1.70.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "c42f454f50a050aaa3f3d200a3ac072e48c18c4bb5356c38be7eee1da1439a43"
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dependencies = [
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"aws-credential-types",
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"aws-runtime",
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@@ -650,9 +650,9 @@ dependencies = [
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[[package]]
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name = "aws-sdk-kms"
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version = "1.63.0"
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version = "1.64.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "a971bfe62ca4a228627a1b74a87a7a142979b20b168d2e2884f4893212ebb715"
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checksum = "c23289881f4071421bbef3688ca43501c4fede796e0cbca942a54f0eb6906fbc"
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dependencies = [
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"aws-credential-types",
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"aws-runtime",
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@@ -664,6 +664,7 @@ dependencies = [
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"aws-smithy-types",
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"aws-types",
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"bytes",
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"fastrand",
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"http 0.2.12",
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"once_cell",
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"regex-lite",
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@@ -672,9 +673,9 @@ dependencies = [
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[[package]]
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name = "aws-sdk-s3"
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version = "1.79.0"
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version = "1.80.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "a8f63ba8f5fca32061c7d62d866ef65470edde38d4c5f8a0ebb8ff40a0521e1c"
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checksum = "3a36b09e8273d89c4f35ea122b83b30e48f906f3b644460d72a7d3656d1be93d"
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dependencies = [
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"aws-credential-types",
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"aws-runtime",
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@@ -707,9 +708,9 @@ dependencies = [
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[[package]]
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name = "aws-sdk-sso"
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version = "1.62.0"
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version = "1.64.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "1d5330ad4e8a1ff49e9f26b738611caa72b105c41d41733801d1a36e8f9de936"
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checksum = "02d4bdb0e5f80f0689e61c77ab678b2b9304af329616af38aef5b6b967b8e736"
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dependencies = [
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"aws-credential-types",
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"aws-runtime",
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@@ -721,6 +722,7 @@ dependencies = [
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"aws-smithy-types",
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"aws-types",
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"bytes",
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"fastrand",
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"http 0.2.12",
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"once_cell",
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"regex-lite",
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@@ -729,9 +731,9 @@ dependencies = [
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[[package]]
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name = "aws-sdk-ssooidc"
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version = "1.63.0"
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version = "1.65.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "7956b1a85d49082347a7d17daa2e32df191f3e23c03d47294b99f95413026a78"
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checksum = "acbbb3ce8da257aedbccdcb1aadafbbb6a5fe9adf445db0e1ea897bdc7e22d08"
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dependencies = [
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"aws-credential-types",
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"aws-runtime",
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@@ -743,6 +745,7 @@ dependencies = [
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"aws-smithy-types",
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"aws-types",
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"bytes",
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"fastrand",
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"http 0.2.12",
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"once_cell",
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"regex-lite",
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@@ -751,9 +754,9 @@ dependencies = [
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[[package]]
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name = "aws-sdk-sts"
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version = "1.63.0"
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version = "1.65.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "065c533fbe6f84962af33fcf02b0350b7c1f79285baab5924615d2be3b232855"
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checksum = "96a78a8f50a1630db757b60f679c8226a8a70ee2ab5f5e6e51dc67f6c61c7cfd"
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dependencies = [
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"aws-credential-types",
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"aws-runtime",
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@@ -766,6 +769,7 @@ dependencies = [
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"aws-smithy-types",
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"aws-smithy-xml",
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"aws-types",
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"fastrand",
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"http 0.2.12",
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"once_cell",
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"regex-lite",
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@@ -869,9 +873,9 @@ dependencies = [
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[[package]]
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name = "aws-smithy-http-client"
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version = "1.0.0"
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version = "1.0.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "0497ef5d53065b7cd6a35e9c1654bd1fefeae5c52900d91d1b188b0af0f29324"
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checksum = "8aff1159006441d02e57204bf57a1b890ba68bedb6904ffd2873c1c4c11c546b"
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dependencies = [
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"aws-smithy-async",
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"aws-smithy-runtime-api",
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@@ -904,6 +908,16 @@ dependencies = [
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"aws-smithy-types",
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]
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[[package]]
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name = "aws-smithy-observability"
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version = "0.1.2"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "445d065e76bc1ef54963db400319f1dd3ebb3e0a74af20f7f7630625b0cc7cc0"
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dependencies = [
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"aws-smithy-runtime-api",
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"once_cell",
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]
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[[package]]
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name = "aws-smithy-query"
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version = "0.60.7"
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@@ -916,13 +930,14 @@ dependencies = [
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[[package]]
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name = "aws-smithy-runtime"
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version = "1.8.0"
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version = "1.8.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "f6328865e36c6fd970094ead6b05efd047d3a80ec5fc3be5e743910da9f2ebf8"
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checksum = "0152749e17ce4d1b47c7747bdfec09dac1ccafdcbc741ebf9daa2a373356730f"
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dependencies = [
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"aws-smithy-async",
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"aws-smithy-http",
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"aws-smithy-http-client",
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"aws-smithy-observability",
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"aws-smithy-runtime-api",
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"aws-smithy-types",
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"bytes",
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@@ -2537,9 +2552,9 @@ dependencies = [
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[[package]]
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name = "event-listener-strategy"
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version = "0.5.3"
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version = "0.5.4"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "3c3e4e0dd3673c1139bf041f3008816d9cf2946bbfac2945c09e523b8d7b05b2"
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checksum = "8be9f3dfaaffdae2972880079a491a1a8bb7cbed0b8dd7a347f668b4150a3b93"
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dependencies = [
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"event-listener 5.4.0",
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"pin-project-lite",
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@@ -3367,9 +3382,9 @@ dependencies = [
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[[package]]
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name = "icu_locid_transform_data"
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version = "1.5.0"
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version = "1.5.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "fdc8ff3388f852bede6b579ad4e978ab004f139284d7b28715f773507b946f6e"
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checksum = "7515e6d781098bf9f7205ab3fc7e9709d34554ae0b21ddbcb5febfa4bc7df11d"
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[[package]]
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name = "icu_normalizer"
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@@ -3391,9 +3406,9 @@ dependencies = [
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[[package]]
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name = "icu_normalizer_data"
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version = "1.5.0"
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version = "1.5.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "f8cafbf7aa791e9b22bec55a167906f9e1215fd475cd22adfcf660e03e989516"
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checksum = "c5e8338228bdc8ab83303f16b797e177953730f601a96c25d10cb3ab0daa0cb7"
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[[package]]
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name = "icu_properties"
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@@ -3412,9 +3427,9 @@ dependencies = [
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[[package]]
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name = "icu_properties_data"
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version = "1.5.0"
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version = "1.5.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "67a8effbc3dd3e4ba1afa8ad918d5684b8868b3b26500753effea8d2eed19569"
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checksum = "85fb8799753b75aee8d2a21d7c14d9f38921b54b3dbda10f5a3c7a7b82dba5e2"
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[[package]]
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name = "icu_provider"
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@@ -4035,7 +4050,7 @@ dependencies = [
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[[package]]
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name = "lancedb"
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version = "0.18.2-beta.0"
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version = "0.18.3-beta.0"
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dependencies = [
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"arrow",
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"arrow-array",
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@@ -4122,7 +4137,7 @@ dependencies = [
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[[package]]
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name = "lancedb-node"
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version = "0.18.2-beta.0"
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version = "0.18.3-beta.0"
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dependencies = [
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"arrow-array",
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"arrow-ipc",
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@@ -4147,7 +4162,7 @@ dependencies = [
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[[package]]
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name = "lancedb-nodejs"
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version = "0.18.2-beta.0"
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version = "0.18.3-beta.0"
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dependencies = [
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"arrow-array",
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"arrow-ipc",
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@@ -4165,7 +4180,7 @@ dependencies = [
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[[package]]
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name = "lancedb-python"
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version = "0.21.2-beta.0"
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version = "0.21.3-beta.0"
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dependencies = [
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"arrow",
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"env_logger",
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@@ -4902,9 +4917,9 @@ dependencies = [
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[[package]]
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name = "once_cell"
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version = "1.21.1"
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version = "1.21.3"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "d75b0bedcc4fe52caa0e03d9f1151a323e4aa5e2d78ba3580400cd3c9e2bc4bc"
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checksum = "42f5e15c9953c5e4ccceeb2e7382a716482c34515315f7b03532b8b4e8393d2d"
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[[package]]
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name = "oneshot"
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@@ -5727,9 +5742,9 @@ dependencies = [
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[[package]]
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name = "quick-xml"
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version = "0.37.2"
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version = "0.37.3"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "165859e9e55f79d67b96c5d96f4e88b6f2695a1972849c15a6a3f5c59fc2c003"
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checksum = "bf763ab1c7a3aa408be466efc86efe35ed1bd3dd74173ed39d6b0d0a6f0ba148"
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dependencies = [
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"memchr",
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"serde",
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@@ -5777,9 +5792,9 @@ dependencies = [
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[[package]]
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name = "quinn-udp"
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version = "0.5.10"
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version = "0.5.11"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "e46f3055866785f6b92bc6164b76be02ca8f2eb4b002c0354b28cf4c119e5944"
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checksum = "541d0f57c6ec747a90738a52741d3221f7960e8ac2f0ff4b1a63680e033b4ab5"
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dependencies = [
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"cfg_aliases",
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"libc",
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@@ -6269,7 +6284,7 @@ dependencies = [
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"once_cell",
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"ring",
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"rustls-pki-types",
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"rustls-webpki 0.103.0",
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"rustls-webpki 0.103.1",
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"subtle",
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"zeroize",
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]
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@@ -6337,9 +6352,9 @@ dependencies = [
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[[package]]
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name = "rustls-webpki"
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version = "0.103.0"
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version = "0.103.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "0aa4eeac2588ffff23e9d7a7e9b3f971c5fb5b7ebc9452745e0c232c64f83b2f"
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checksum = "fef8b8769aaccf73098557a87cd1816b4f9c7c16811c9c77142aa695c16f2c03"
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dependencies = [
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"aws-lc-rs",
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"ring",
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@@ -30,6 +30,53 @@ protected inner: Query | Promise<Query>;
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## Methods
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### analyzePlan()
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```ts
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analyzePlan(): Promise<string>
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```
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Executes the query and returns the physical query plan annotated with runtime metrics.
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This is useful for debugging and performance analysis, as it shows how the query was executed
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and includes metrics such as elapsed time, rows processed, and I/O statistics.
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#### Returns
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`Promise`<`string`>
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A query execution plan with runtime metrics for each step.
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#### Example
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```ts
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import * as lancedb from "@lancedb/lancedb"
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const db = await lancedb.connect("./.lancedb");
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const table = await db.createTable("my_table", [
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{ vector: [1.1, 0.9], id: "1" },
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]);
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const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
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Example output (with runtime metrics inlined):
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AnalyzeExec verbose=true, metrics=[]
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ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
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Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
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CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
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GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
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FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
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SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
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KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
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LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
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```
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#### Inherited from
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[`QueryBase`](QueryBase.md).[`analyzePlan`](QueryBase.md#analyzeplan)
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***
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### execute()
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```ts
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@@ -36,6 +36,49 @@ protected inner: NativeQueryType | Promise<NativeQueryType>;
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## Methods
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### analyzePlan()
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```ts
|
||||
analyzePlan(): Promise<string>
|
||||
```
|
||||
|
||||
Executes the query and returns the physical query plan annotated with runtime metrics.
|
||||
|
||||
This is useful for debugging and performance analysis, as it shows how the query was executed
|
||||
and includes metrics such as elapsed time, rows processed, and I/O statistics.
|
||||
|
||||
#### Returns
|
||||
|
||||
`Promise`<`string`>
|
||||
|
||||
A query execution plan with runtime metrics for each step.
|
||||
|
||||
#### Example
|
||||
|
||||
```ts
|
||||
import * as lancedb from "@lancedb/lancedb"
|
||||
|
||||
const db = await lancedb.connect("./.lancedb");
|
||||
const table = await db.createTable("my_table", [
|
||||
{ vector: [1.1, 0.9], id: "1" },
|
||||
]);
|
||||
|
||||
const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
|
||||
|
||||
Example output (with runtime metrics inlined):
|
||||
AnalyzeExec verbose=true, metrics=[]
|
||||
ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
|
||||
Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
|
||||
CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
|
||||
GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
|
||||
FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
|
||||
SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
|
||||
KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
|
||||
LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
|
||||
```
|
||||
|
||||
***
|
||||
|
||||
### execute()
|
||||
|
||||
```ts
|
||||
|
||||
@@ -48,6 +48,53 @@ addQueryVector(vector): VectorQuery
|
||||
|
||||
***
|
||||
|
||||
### analyzePlan()
|
||||
|
||||
```ts
|
||||
analyzePlan(): Promise<string>
|
||||
```
|
||||
|
||||
Executes the query and returns the physical query plan annotated with runtime metrics.
|
||||
|
||||
This is useful for debugging and performance analysis, as it shows how the query was executed
|
||||
and includes metrics such as elapsed time, rows processed, and I/O statistics.
|
||||
|
||||
#### Returns
|
||||
|
||||
`Promise`<`string`>
|
||||
|
||||
A query execution plan with runtime metrics for each step.
|
||||
|
||||
#### Example
|
||||
|
||||
```ts
|
||||
import * as lancedb from "@lancedb/lancedb"
|
||||
|
||||
const db = await lancedb.connect("./.lancedb");
|
||||
const table = await db.createTable("my_table", [
|
||||
{ vector: [1.1, 0.9], id: "1" },
|
||||
]);
|
||||
|
||||
const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
|
||||
|
||||
Example output (with runtime metrics inlined):
|
||||
AnalyzeExec verbose=true, metrics=[]
|
||||
ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
|
||||
Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
|
||||
CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
|
||||
GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
|
||||
FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
|
||||
SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
|
||||
KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
|
||||
LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
|
||||
```
|
||||
|
||||
#### Inherited from
|
||||
|
||||
[`QueryBase`](QueryBase.md).[`analyzePlan`](QueryBase.md#analyzeplan)
|
||||
|
||||
***
|
||||
|
||||
### bypassVectorIndex()
|
||||
|
||||
```ts
|
||||
|
||||
@@ -36,3 +36,9 @@ print the resolved query plan. You can use the `explain_plan` method to do this:
|
||||
* Python Sync: [LanceQueryBuilder.explain_plan][lancedb.query.LanceQueryBuilder.explain_plan]
|
||||
* Python Async: [AsyncQueryBase.explain_plan][lancedb.query.AsyncQueryBase.explain_plan]
|
||||
* Node @lancedb/lancedb: [LanceQueryBuilder.explainPlan](/lancedb/js/classes/QueryBase/#explainplan)
|
||||
|
||||
To understand how a query was actually executed—including metrics like execution time, number of rows processed, I/O stats, and more—use the analyze_plan method. This executes the query and returns a physical execution plan annotated with runtime metrics, making it especially helpful for performance tuning and debugging.
|
||||
|
||||
* Python Sync: [LanceQueryBuilder.analyze_plan][lancedb.query.LanceQueryBuilder.analyze_plan]
|
||||
* Python Async: [AsyncQueryBase.analyze_plan][lancedb.query.AsyncQueryBase.analyze_plan]
|
||||
* Node @lancedb/lancedb: [LanceQueryBuilder.analyzePlan](/lancedb/js/classes/QueryBase/#analyzePlan)
|
||||
|
||||
@@ -633,6 +633,23 @@ describe("When creating an index", () => {
|
||||
expect(plan2).not.toMatch("LanceScan");
|
||||
});
|
||||
|
||||
it("should be able to run analyze plan", async () => {
|
||||
await tbl.createIndex("vec");
|
||||
await tbl.add([
|
||||
{
|
||||
id: 300,
|
||||
vec: Array(32)
|
||||
.fill(1)
|
||||
.map(() => Math.random()),
|
||||
tags: [],
|
||||
},
|
||||
]);
|
||||
|
||||
const plan = await tbl.query().nearestTo(queryVec).analyzePlan();
|
||||
expect(plan).toMatch("AnalyzeExec");
|
||||
expect(plan).toMatch("metrics=");
|
||||
});
|
||||
|
||||
it("should be able to query with row id", async () => {
|
||||
const results = await tbl
|
||||
.query()
|
||||
@@ -1346,6 +1363,30 @@ describe("when calling explainPlan", () => {
|
||||
});
|
||||
});
|
||||
|
||||
describe("when calling analyzePlan", () => {
|
||||
let tmpDir: tmp.DirResult;
|
||||
let table: Table;
|
||||
let queryVec: number[];
|
||||
beforeEach(async () => {
|
||||
tmpDir = tmp.dirSync({ unsafeCleanup: true });
|
||||
const con = await connect(tmpDir.name);
|
||||
table = await con.createTable("vectors", [{ id: 1, vector: [1.1, 0.9] }]);
|
||||
});
|
||||
|
||||
afterEach(() => {
|
||||
tmpDir.removeCallback();
|
||||
});
|
||||
|
||||
it("retrieves runtime metrics", async () => {
|
||||
queryVec = Array(2)
|
||||
.fill(1)
|
||||
.map(() => Math.random());
|
||||
const plan = await table.query().nearestTo(queryVec).analyzePlan();
|
||||
console.log("Query Plan:\n", plan); // <--- Print the plan
|
||||
expect(plan).toMatch("AnalyzeExec");
|
||||
});
|
||||
});
|
||||
|
||||
describe("column name options", () => {
|
||||
let tmpDir: tmp.DirResult;
|
||||
let table: Table;
|
||||
|
||||
@@ -348,6 +348,43 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
|
||||
return this.inner.explainPlan(verbose);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Executes the query and returns the physical query plan annotated with runtime metrics.
|
||||
*
|
||||
* This is useful for debugging and performance analysis, as it shows how the query was executed
|
||||
* and includes metrics such as elapsed time, rows processed, and I/O statistics.
|
||||
*
|
||||
* @example
|
||||
* import * as lancedb from "@lancedb/lancedb"
|
||||
*
|
||||
* const db = await lancedb.connect("./.lancedb");
|
||||
* const table = await db.createTable("my_table", [
|
||||
* { vector: [1.1, 0.9], id: "1" },
|
||||
* ]);
|
||||
*
|
||||
* const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
|
||||
*
|
||||
* Example output (with runtime metrics inlined):
|
||||
* AnalyzeExec verbose=true, metrics=[]
|
||||
* ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
|
||||
* Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
|
||||
* CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
|
||||
* GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
|
||||
* FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
|
||||
* SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
|
||||
* KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
|
||||
* LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
|
||||
*
|
||||
* @returns A query execution plan with runtime metrics for each step.
|
||||
*/
|
||||
async analyzePlan(): Promise<string> {
|
||||
if (this.inner instanceof Promise) {
|
||||
return this.inner.then((inner) => inner.analyzePlan());
|
||||
} else {
|
||||
return this.inner.analyzePlan();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -114,6 +114,16 @@ impl Query {
|
||||
))
|
||||
})
|
||||
}
|
||||
|
||||
#[napi(catch_unwind)]
|
||||
pub async fn analyze_plan(&self) -> napi::Result<String> {
|
||||
self.inner.analyze_plan().await.map_err(|e| {
|
||||
napi::Error::from_reason(format!(
|
||||
"Failed to execute analyze plan: {}",
|
||||
convert_error(&e)
|
||||
))
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
#[napi]
|
||||
@@ -259,4 +269,14 @@ impl VectorQuery {
|
||||
))
|
||||
})
|
||||
}
|
||||
|
||||
#[napi(catch_unwind)]
|
||||
pub async fn analyze_plan(&self) -> napi::Result<String> {
|
||||
self.inner.analyze_plan().await.map_err(|e| {
|
||||
napi::Error::from_reason(format!(
|
||||
"Failed to execute analyze plan: {}",
|
||||
convert_error(&e)
|
||||
))
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
@@ -96,6 +96,7 @@ class Query:
|
||||
def nearest_to_text(self, query: dict) -> FTSQuery: ...
|
||||
async def execute(self, max_batch_length: Optional[int]) -> RecordBatchStream: ...
|
||||
async def explain_plan(self, verbose: Optional[bool]) -> str: ...
|
||||
async def analyze_plan(self) -> str: ...
|
||||
def to_query_request(self) -> PyQueryRequest: ...
|
||||
|
||||
class FTSQuery:
|
||||
|
||||
@@ -659,6 +659,44 @@ class LanceQueryBuilder(ABC):
|
||||
""" # noqa: E501
|
||||
return self._table._explain_plan(self.to_query_object(), verbose=verbose)
|
||||
|
||||
def analyze_plan(self) -> str:
|
||||
"""
|
||||
Run the query and return its execution plan with runtime metrics.
|
||||
|
||||
This returns detailed metrics for each step, such as elapsed time,
|
||||
rows processed, bytes read, and I/O stats. It is useful for debugging
|
||||
and performance tuning.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> import lancedb
|
||||
>>> db = lancedb.connect("./.lancedb")
|
||||
>>> table = db.create_table("my_table", [{"vector": [99.0, 99]}])
|
||||
>>> query = [100, 100]
|
||||
>>> plan = table.search(query).analyze_plan()
|
||||
>>> print(plan) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
|
||||
AnalyzeExec verbose=true, metrics=[]
|
||||
ProjectionExec: expr=[...], metrics=[...]
|
||||
GlobalLimitExec: skip=0, fetch=10, metrics=[...]
|
||||
FilterExec: _distance@2 IS NOT NULL,
|
||||
metrics=[output_rows=..., elapsed_compute=...]
|
||||
SortExec: TopK(fetch=10), expr=[...],
|
||||
preserve_partitioning=[...],
|
||||
metrics=[output_rows=..., elapsed_compute=..., row_replacements=...]
|
||||
KNNVectorDistance: metric=l2,
|
||||
metrics=[output_rows=..., elapsed_compute=..., output_batches=...]
|
||||
LanceScan: uri=..., projection=[vector], row_id=true,
|
||||
row_addr=false, ordered=false,
|
||||
metrics=[output_rows=..., elapsed_compute=...,
|
||||
bytes_read=..., iops=..., requests=...]
|
||||
|
||||
Returns
|
||||
-------
|
||||
plan : str
|
||||
The physical query execution plan with runtime metrics.
|
||||
"""
|
||||
return self._table._analyze_plan(self.to_query_object())
|
||||
|
||||
def vector(self, vector: Union[np.ndarray, list]) -> Self:
|
||||
"""Set the vector to search for.
|
||||
|
||||
@@ -1941,6 +1979,15 @@ class AsyncQueryBase(object):
|
||||
""" # noqa: E501
|
||||
return await self._inner.explain_plan(verbose)
|
||||
|
||||
async def analyze_plan(self):
|
||||
"""Execute the query and display with runtime metrics.
|
||||
|
||||
Returns
|
||||
-------
|
||||
plan : str
|
||||
"""
|
||||
return await self._inner.analyze_plan()
|
||||
|
||||
|
||||
class AsyncQuery(AsyncQueryBase):
|
||||
def __init__(self, inner: LanceQuery):
|
||||
@@ -2510,7 +2557,7 @@ class AsyncHybridQuery(AsyncQueryBase, AsyncVectorQueryBase):
|
||||
|
||||
Returns
|
||||
-------
|
||||
plan
|
||||
plan : str
|
||||
""" # noqa: E501
|
||||
|
||||
results = ["Vector Search Plan:"]
|
||||
@@ -2519,3 +2566,23 @@ class AsyncHybridQuery(AsyncQueryBase, AsyncVectorQueryBase):
|
||||
results.append(await self._inner.to_fts_query().explain_plan(verbose))
|
||||
|
||||
return "\n".join(results)
|
||||
|
||||
async def analyze_plan(self):
|
||||
"""
|
||||
Execute the query and return the physical execution plan with runtime metrics.
|
||||
|
||||
This runs both the vector and FTS (full-text search) queries and returns
|
||||
detailed metrics for each step of execution—such as rows processed,
|
||||
elapsed time, I/O stats, and more. It’s useful for debugging and
|
||||
performance analysis.
|
||||
|
||||
Returns
|
||||
-------
|
||||
plan : str
|
||||
"""
|
||||
results = ["Vector Search Query:"]
|
||||
results.append(await self._inner.to_vector_query().analyze_plan())
|
||||
results.append("FTS Search Query:")
|
||||
results.append(await self._inner.to_fts_query().analyze_plan())
|
||||
|
||||
return "\n".join(results)
|
||||
|
||||
@@ -371,6 +371,9 @@ class RemoteTable(Table):
|
||||
def _explain_plan(self, query: Query, verbose: Optional[bool] = False) -> str:
|
||||
return LOOP.run(self._table._explain_plan(query, verbose))
|
||||
|
||||
def _analyze_plan(self, query: Query) -> str:
|
||||
return LOOP.run(self._table._analyze_plan(query))
|
||||
|
||||
def merge_insert(self, on: Union[str, Iterable[str]]) -> LanceMergeInsertBuilder:
|
||||
"""Returns a [`LanceMergeInsertBuilder`][lancedb.merge.LanceMergeInsertBuilder]
|
||||
that can be used to create a "merge insert" operation.
|
||||
|
||||
@@ -1010,6 +1010,9 @@ class Table(ABC):
|
||||
@abstractmethod
|
||||
def _explain_plan(self, query: Query, verbose: Optional[bool] = False) -> str: ...
|
||||
|
||||
@abstractmethod
|
||||
def _analyze_plan(self, query: Query) -> str: ...
|
||||
|
||||
@abstractmethod
|
||||
def _do_merge(
|
||||
self,
|
||||
@@ -2318,6 +2321,9 @@ class LanceTable(Table):
|
||||
def _explain_plan(self, query: Query, verbose: Optional[bool] = False) -> str:
|
||||
return LOOP.run(self._table._explain_plan(query, verbose))
|
||||
|
||||
def _analyze_plan(self, query: Query) -> str:
|
||||
return LOOP.run(self._table._analyze_plan(query))
|
||||
|
||||
def _do_merge(
|
||||
self,
|
||||
merge: LanceMergeInsertBuilder,
|
||||
@@ -3388,6 +3394,11 @@ class AsyncTable:
|
||||
async_query = self._sync_query_to_async(query)
|
||||
return await async_query.explain_plan(verbose)
|
||||
|
||||
async def _analyze_plan(self, query: Query) -> str:
|
||||
# This method is used by the sync table
|
||||
async_query = self._sync_query_to_async(query)
|
||||
return await async_query.analyze_plan()
|
||||
|
||||
async def _do_merge(
|
||||
self,
|
||||
merge: LanceMergeInsertBuilder,
|
||||
|
||||
@@ -114,6 +114,16 @@ async def test_explain_plan(table: AsyncTable):
|
||||
assert "LanceScan" in plan
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_analyze_plan(table: AsyncTable):
|
||||
res = await (
|
||||
table.query().nearest_to_text("dog").nearest_to([0.1, 0.1]).analyze_plan()
|
||||
)
|
||||
|
||||
assert "AnalyzeExec" in res
|
||||
assert "metrics=" in res
|
||||
|
||||
|
||||
def test_normalize_scores():
|
||||
cases = [
|
||||
(pa.array([0.1, 0.4]), pa.array([0.0, 1.0])),
|
||||
|
||||
@@ -702,6 +702,20 @@ async def test_fast_search_async(tmp_path):
|
||||
assert "LanceScan" not in plan
|
||||
|
||||
|
||||
def test_analyze_plan(table):
|
||||
q = LanceVectorQueryBuilder(table, [0, 0], "vector")
|
||||
res = q.analyze_plan()
|
||||
assert "AnalyzeExec" in res
|
||||
assert "metrics=" in res
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_analyze_plan_async(table_async: AsyncTable):
|
||||
res = await table_async.query().nearest_to(pa.array([1, 2])).analyze_plan()
|
||||
assert "AnalyzeExec" in res
|
||||
assert "metrics=" in res
|
||||
|
||||
|
||||
def test_explain_plan(table):
|
||||
q = LanceVectorQueryBuilder(table, [0, 0], "vector")
|
||||
plan = q.explain_plan(verbose=True)
|
||||
|
||||
@@ -281,6 +281,16 @@ impl Query {
|
||||
})
|
||||
}
|
||||
|
||||
pub fn analyze_plan(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
|
||||
let inner = self_.inner.clone();
|
||||
future_into_py(self_.py(), async move {
|
||||
inner
|
||||
.analyze_plan()
|
||||
.await
|
||||
.map_err(|e| PyRuntimeError::new_err(e.to_string()))
|
||||
})
|
||||
}
|
||||
|
||||
pub fn to_query_request(&self) -> PyQueryRequest {
|
||||
PyQueryRequest::from(AnyQuery::Query(self.inner.clone().into_request()))
|
||||
}
|
||||
@@ -365,6 +375,16 @@ impl FTSQuery {
|
||||
})
|
||||
}
|
||||
|
||||
pub fn analyze_plan(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
|
||||
let inner = self_.inner.clone();
|
||||
future_into_py(self_.py(), async move {
|
||||
inner
|
||||
.analyze_plan()
|
||||
.await
|
||||
.map_err(|e| PyRuntimeError::new_err(e.to_string()))
|
||||
})
|
||||
}
|
||||
|
||||
pub fn get_query(&self) -> String {
|
||||
self.fts_query.query.clone()
|
||||
}
|
||||
@@ -480,6 +500,16 @@ impl VectorQuery {
|
||||
})
|
||||
}
|
||||
|
||||
pub fn analyze_plan(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
|
||||
let inner = self_.inner.clone();
|
||||
future_into_py(self_.py(), async move {
|
||||
inner
|
||||
.analyze_plan()
|
||||
.await
|
||||
.map_err(|e| PyRuntimeError::new_err(e.to_string()))
|
||||
})
|
||||
}
|
||||
|
||||
pub fn nearest_to_text(&mut self, query: Bound<'_, PyDict>) -> PyResult<HybridQuery> {
|
||||
let base_query = self.inner.clone().into_plain();
|
||||
let fts_query = Query::new(base_query).nearest_to_text(query)?;
|
||||
|
||||
@@ -579,6 +579,15 @@ pub trait ExecutableQuery {
|
||||
) -> impl Future<Output = Result<SendableRecordBatchStream>> + Send;
|
||||
|
||||
fn explain_plan(&self, verbose: bool) -> impl Future<Output = Result<String>> + Send;
|
||||
|
||||
fn analyze_plan(&self) -> impl Future<Output = Result<String>> + Send {
|
||||
self.analyze_plan_with_options(QueryExecutionOptions::default())
|
||||
}
|
||||
|
||||
fn analyze_plan_with_options(
|
||||
&self,
|
||||
options: QueryExecutionOptions,
|
||||
) -> impl Future<Output = Result<String>> + Send;
|
||||
}
|
||||
|
||||
/// A query filter that can be applied to a query
|
||||
@@ -765,6 +774,11 @@ impl ExecutableQuery for Query {
|
||||
let query = AnyQuery::Query(self.request.clone());
|
||||
self.parent.explain_plan(&query, verbose).await
|
||||
}
|
||||
|
||||
async fn analyze_plan_with_options(&self, options: QueryExecutionOptions) -> Result<String> {
|
||||
let query = AnyQuery::Query(self.request.clone());
|
||||
self.parent.analyze_plan(&query, options).await
|
||||
}
|
||||
}
|
||||
|
||||
/// A request for a nearest-neighbors search into a table
|
||||
@@ -1089,6 +1103,11 @@ impl ExecutableQuery for VectorQuery {
|
||||
let query = AnyQuery::VectorQuery(self.request.clone());
|
||||
self.parent.explain_plan(&query, verbose).await
|
||||
}
|
||||
|
||||
async fn analyze_plan_with_options(&self, options: QueryExecutionOptions) -> Result<String> {
|
||||
let query = AnyQuery::VectorQuery(self.request.clone());
|
||||
self.parent.analyze_plan(&query, options).await
|
||||
}
|
||||
}
|
||||
|
||||
impl HasQuery for VectorQuery {
|
||||
@@ -1370,6 +1389,31 @@ mod tests {
|
||||
}
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_analyze_plan() {
|
||||
let tmp_dir = tempdir().unwrap();
|
||||
let table = make_test_table(&tmp_dir).await;
|
||||
|
||||
let result = table.query().analyze_plan().await.unwrap();
|
||||
assert!(result.contains("metrics="));
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_analyze_plan_with_options() {
|
||||
let tmp_dir = tempdir().unwrap();
|
||||
let table = make_test_table(&tmp_dir).await;
|
||||
|
||||
let result = table
|
||||
.query()
|
||||
.analyze_plan_with_options(QueryExecutionOptions {
|
||||
max_batch_length: 10,
|
||||
..Default::default()
|
||||
})
|
||||
.await
|
||||
.unwrap();
|
||||
assert!(result.contains("metrics="));
|
||||
}
|
||||
|
||||
fn assert_plan_exists(plan: &Arc<dyn ExecutionPlan>, name: &str) -> bool {
|
||||
if plan.name() == name {
|
||||
return true;
|
||||
|
||||
@@ -614,6 +614,48 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
|
||||
Ok(final_plan)
|
||||
}
|
||||
|
||||
async fn analyze_plan(
|
||||
&self,
|
||||
query: &AnyQuery,
|
||||
_options: QueryExecutionOptions,
|
||||
) -> Result<String> {
|
||||
let request = self
|
||||
.client
|
||||
.post(&format!("/v1/table/{}/analyze_plan/", self.name));
|
||||
|
||||
let query_bodies = self.prepare_query_bodies(query).await?;
|
||||
let requests: Vec<reqwest::RequestBuilder> = query_bodies
|
||||
.into_iter()
|
||||
.map(|body| request.try_clone().unwrap().json(&body))
|
||||
.collect();
|
||||
|
||||
let futures = requests.into_iter().map(|req| async move {
|
||||
let (request_id, response) = self.client.send(req, true).await?;
|
||||
let response = self.check_table_response(&request_id, response).await?;
|
||||
let body = response.text().await.err_to_http(request_id.clone())?;
|
||||
|
||||
serde_json::from_str(&body).map_err(|e| Error::Http {
|
||||
source: format!("Failed to execute analyze plan: {}", e).into(),
|
||||
request_id,
|
||||
status_code: None,
|
||||
})
|
||||
});
|
||||
|
||||
let analyze_result_texts = futures::future::try_join_all(futures).await?;
|
||||
let final_analyze = if analyze_result_texts.len() > 1 {
|
||||
analyze_result_texts
|
||||
.into_iter()
|
||||
.enumerate()
|
||||
.map(|(i, plan)| format!("--- Query #{} ---\n{}", i + 1, plan))
|
||||
.collect::<Vec<_>>()
|
||||
.join("\n\n")
|
||||
} else {
|
||||
analyze_result_texts.into_iter().next().unwrap_or_default()
|
||||
};
|
||||
|
||||
Ok(final_analyze)
|
||||
}
|
||||
|
||||
async fn update(&self, update: UpdateBuilder) -> Result<u64> {
|
||||
self.check_mutable().await?;
|
||||
let request = self
|
||||
|
||||
@@ -33,7 +33,7 @@ use lance::dataset::{
|
||||
use lance::dataset::{MergeInsertBuilder as LanceMergeInsertBuilder, WhenNotMatchedBySource};
|
||||
use lance::index::vector::utils::infer_vector_dim;
|
||||
use lance::io::WrappingObjectStore;
|
||||
use lance_datafusion::exec::execute_plan;
|
||||
use lance_datafusion::exec::{analyze_plan as lance_analyze_plan, execute_plan};
|
||||
use lance_datafusion::utils::StreamingWriteSource;
|
||||
use lance_index::vector::hnsw::builder::HnswBuildParams;
|
||||
use lance_index::vector::ivf::IvfBuildParams;
|
||||
@@ -433,6 +433,12 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
|
||||
|
||||
Ok(format!("{}", display.indent(verbose)))
|
||||
}
|
||||
async fn analyze_plan(
|
||||
&self,
|
||||
query: &AnyQuery,
|
||||
options: QueryExecutionOptions,
|
||||
) -> Result<String>;
|
||||
|
||||
/// Add new records to the table.
|
||||
async fn add(
|
||||
&self,
|
||||
@@ -2192,6 +2198,15 @@ impl BaseTable for NativeTable {
|
||||
self.generic_query(query, options).await
|
||||
}
|
||||
|
||||
async fn analyze_plan(
|
||||
&self,
|
||||
query: &AnyQuery,
|
||||
options: QueryExecutionOptions,
|
||||
) -> Result<String> {
|
||||
let plan = self.create_plan(query, options).await?;
|
||||
Ok(lance_analyze_plan(plan, Default::default()).await?)
|
||||
}
|
||||
|
||||
async fn merge_insert(
|
||||
&self,
|
||||
params: MergeInsertBuilder,
|
||||
|
||||
Reference in New Issue
Block a user