miller/pkg/input/record_reader_csvlite.go
John Kerl 74a08b15c0
Batch-arena field allocation for line-based readers (#2082)
* Lazy per-record hashing: ~15-30% faster on common workloads

Records (NewMlrmapAsRecord) eagerly allocated and populated a
map[string]*MlrmapEntry on construction whenever hashRecords was true
(the default). For streaming verbs that never look records up by key
(e.g. `mlr cat`) that map is pure overhead: a heap allocation plus N
map-inserts per record, and N more pointer-heavy objects for the GC to
scan. Profiling 1M-record CSV shows runtime allocation/GC machinery
dominating every workload, and `--no-hash-records` was 25-30% faster --
but that flag makes wide-record lookups O(n), the regression that
motivated hashing in #1506.

Make record hashing lazy instead: allocate no index up front; build it
in findEntry on the first lookup, and only when the record is wide
enough (FieldCount >= mlrmapHashThreshold) that linear search would
hurt. Narrow records and never-looked-up records never pay for a map;
wide records that are actually queried still get hash-accelerated
lookups, matching the old eager-hash default. DSL maps (NewMlrmap) keep
eager hashing to limit the behavioral surface.

This is transparent: findEntry already fell back to linear scan when
keysToEntries was nil, and every mutator already guarded on
keysToEntries != nil.

Measured (big.csv, 1M x 7 cols, default flags, best of 3):
  cat    0.62 -> 0.47  (~24%)
  put    1.08 -> 0.82  (~24%)
  stats1 0.66 -> 0.57  (~14%)
  sort   2.9  -> 2.0   (~30%)

Wide-column case protected: 60-col file with field lookups, lazy (1.42s)
matches old eager default (1.40s) and beats pure linear (1.55s).

Verified: go test ./pkg/... and full regression suite pass; output is
byte-identical to forced --hash-records for sort, stats1, cut,
wide-column put, and duplicate-key dedupe.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* Batch-arena field allocation for line-based readers (approach B)

Following the lazy-hashing commit, profiling showed the dominant remaining
cost in read/write-bound workloads is allocation *operations* (not bytes):
each input field allocated two heap objects -- an Mlrval (FromDeferredType)
and an MlrmapEntry. For 1M x 7-field CSV that is ~13.4M of ~18.6M total
allocations.

Introduce mlrval.RecordArena, a per-batch slab allocator: a reader draws
each field's entry and value from contiguous []MlrmapEntry / []Mlrval slabs,
turning two allocations per field into roughly two per slab. The arena grows
on demand, so the size hint need not be exact; on duplicate keys it mirrors
PutReferenceMaybeDedupe semantics. findEntry/linkNewEntry already supported
externally-constructed entries, so this is transparent.

Wired into every line-based reader that builds records from deferred-type
strings: CSV, CSV-lite, TSV, DKVP, NIDX, PPRINT, XTAB, DKVPX. (JSON values
arrive already typed and are unaffected.) Readers with inline batch loops use
a local arena; those that build records via a helper (DKVP/NIDX line
splitter, XTAB stanza) hold the arena on the reader struct, reset per batch
and also initialized in the constructor so direct/test callers never see nil.

Measured (big.*, 1M records, default flags, cat, best of 3):
  csv   0.46 -> 0.27  (~41%)
  dkvp  0.75 -> 0.46  (~39%)
  nidx  1.92 -> 1.58  (~18%)
  (xtab ~flat: dominated by stanza parse/emit, not field allocation)

For cat the allocation-object count drops from ~18.6M to ~4.85M and peak RSS
from ~402MB to ~237MB (slabs are compact and freed as units). Alloc *bytes*
are essentially unchanged -- confirming the cost was per-allocation overhead,
not volume. Streaming and accumulating verbs (put/sort) are unchanged: their
bottleneck is DSL-side allocation / heap scanning, not field construction.

Verified: go test ./pkg/... and full regression suite pass; output is
byte-identical across all formats (hashed and --no-hash-records).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-19 16:40:05 -04:00

386 lines
10 KiB
Go

package input
// Multi-file cases:
//
// a,a a,b c d
// -- FILE1: -- FILE1: -- FILE1: -- FILE1:
// a,b,c a,b,c a,b,c a,b,c
// 1,2,3 1,2,3 1,2,3 1,2,3
// 4,5,6 4,5,6 4,5,6 4,5,6
// -- FILE2: -- FILE2:
// a,b,c d,e,f,g a,b,c d,e,f
// 7,8,9 3,4,5,6 7,8,9 3,4,5
// --OUTPUT: --OUTPUT: --OUTPUT: --OUTPUT:
// a,b,c a,b,c a,b,c a,b,c
// 1,2,3 1,2,3 1,2,3 1,2,3
// 4,5,6 4,5,6 4,5,6 4,5,6
// 7,8,9 7,8,9
// d,e,f,g d,e,f
// 3,4,5,6 3,4,5
import (
"fmt"
"io"
"strconv"
"strings"
"github.com/johnkerl/miller/v6/pkg/cli"
"github.com/johnkerl/miller/v6/pkg/lib"
"github.com/johnkerl/miller/v6/pkg/mlrval"
"github.com/johnkerl/miller/v6/pkg/types"
)
// recordBatchGetterCSV points to either an explicit-CSV-header or
// implicit-CSV-header record-batch getter.
type recordBatchGetterCSV func(
reader *RecordReaderCSVLite,
linesChannel <-chan []string,
filename string,
context *types.Context,
errorChannel chan error,
) (
recordsAndContexts []*types.RecordAndContext,
eof bool,
)
type RecordReaderCSVLite struct {
readerOptions *cli.TReaderOptions
recordsPerBatch int64 // distinct from readerOptions.RecordsPerBatch for join/repl
fieldSplitter iFieldSplitter
recordBatchGetter recordBatchGetterCSV
inputLineNumber int64
headerStrings []string
useVoidRep bool
voidRep string // For pprint output, empty strings are mapped to "-"; this is for reading them back in
}
func NewRecordReaderCSVLite(
readerOptions *cli.TReaderOptions,
recordsPerBatch int64,
) (*RecordReaderCSVLite, error) {
reader := &RecordReaderCSVLite{
readerOptions: readerOptions,
recordsPerBatch: recordsPerBatch,
fieldSplitter: newFieldSplitter(readerOptions),
useVoidRep: false,
voidRep: "",
}
if reader.readerOptions.UseImplicitHeader {
reader.recordBatchGetter = getRecordBatchImplicitCSVHeader
} else {
reader.recordBatchGetter = getRecordBatchExplicitCSVHeader
}
return reader, nil
}
func (reader *RecordReaderCSVLite) Read(
filenames []string,
context types.Context,
readerChannel chan<- []*types.RecordAndContext, // list of *types.RecordAndContext
errorChannel chan error,
downstreamDoneChannel <-chan bool, // for mlr head
) {
if filenames != nil { // nil for mlr -n
if len(filenames) == 0 { // read from stdin
handle, err := lib.OpenStdin(
reader.readerOptions.Prepipe,
reader.readerOptions.PrepipeIsRaw,
reader.readerOptions.FileInputEncoding,
)
if err != nil {
errorChannel <- err
} else {
reader.processHandle(
handle,
"(stdin)",
&context,
readerChannel,
errorChannel,
downstreamDoneChannel,
)
}
} else {
for _, filename := range filenames {
handle, err := lib.OpenFileForRead(
filename,
reader.readerOptions.Prepipe,
reader.readerOptions.PrepipeIsRaw,
reader.readerOptions.FileInputEncoding,
)
if err != nil {
errorChannel <- err
} else {
reader.processHandle(
handle,
filename,
&context,
readerChannel,
errorChannel,
downstreamDoneChannel,
)
handle.Close()
}
}
}
}
readerChannel <- types.NewEndOfStreamMarkerList(&context)
}
func (reader *RecordReaderCSVLite) processHandle(
handle io.Reader,
filename string,
context *types.Context,
readerChannel chan<- []*types.RecordAndContext, // list of *types.RecordAndContext
errorChannel chan error,
downstreamDoneChannel <-chan bool, // for mlr head
) {
context.UpdateForStartOfFile(filename)
reader.inputLineNumber = 0
reader.headerStrings = nil
recordsPerBatch := reader.recordsPerBatch
lineReader := NewLineReader(handle, reader.readerOptions.IRS)
linesChannel := make(chan []string, recordsPerBatch)
go channelizedLineReader(lineReader, linesChannel, downstreamDoneChannel, recordsPerBatch)
for {
recordsAndContexts, eof := reader.recordBatchGetter(reader, linesChannel, filename, context, errorChannel)
if len(recordsAndContexts) > 0 {
readerChannel <- recordsAndContexts
}
if eof {
break
}
}
}
func getRecordBatchExplicitCSVHeader(
reader *RecordReaderCSVLite,
linesChannel <-chan []string,
filename string,
context *types.Context,
errorChannel chan error,
) (
recordsAndContexts []*types.RecordAndContext,
eof bool,
) {
recordsAndContexts = []*types.RecordAndContext{}
dedupeFieldNames := reader.readerOptions.DedupeFieldNames
lines, more := <-linesChannel
if !more {
return recordsAndContexts, true
}
arena := mlrval.NewRecordArena(len(lines) * 8)
for _, line := range lines {
reader.inputLineNumber++
// Strip CSV BOM
if reader.inputLineNumber == 1 {
if strings.HasPrefix(line, CSV_BOM) {
line = strings.Replace(line, CSV_BOM, "", 1)
}
}
// Check for comments-in-data feature
// TODO: function-pointer this away
if reader.readerOptions.CommentHandling != cli.CommentsAreData {
if strings.HasPrefix(line, reader.readerOptions.CommentString) {
if reader.readerOptions.CommentHandling == cli.PassComments {
recordsAndContexts = append(recordsAndContexts, types.NewOutputString(line+"\n", context))
continue
} else if reader.readerOptions.CommentHandling == cli.SkipComments {
continue
}
// else comments are data
}
}
if line == "" {
// Reset to new schema
reader.headerStrings = nil
continue
}
fields := reader.fieldSplitter.Split(line)
if reader.headerStrings == nil {
reader.headerStrings = fields
// Get data lines on subsequent loop iterations
} else {
if !reader.readerOptions.AllowRaggedCSVInput && len(reader.headerStrings) != len(fields) {
err := fmt.Errorf(
"CSV header/data length mismatch %d != %d at filename %s line %d",
len(reader.headerStrings), len(fields), filename, reader.inputLineNumber,
)
errorChannel <- err
return
}
record := mlrval.NewMlrmapAsRecord()
if !reader.readerOptions.AllowRaggedCSVInput {
for i, field := range fields {
if reader.useVoidRep && field == reader.voidRep {
field = ""
}
arena.PutDeferred(record, reader.headerStrings[i], field, dedupeFieldNames)
}
} else {
nh := int64(len(reader.headerStrings))
nd := int64(len(fields))
n := lib.IntMin2(nh, nd)
var i int64
for i = 0; i < n; i++ {
field := fields[i]
if reader.useVoidRep && field == reader.voidRep {
field = ""
}
arena.PutDeferred(record, reader.headerStrings[i], field, dedupeFieldNames)
}
if nh < nd {
// if header shorter than data: use 1-up itoa keys
for i = nh; i < nd; i++ {
key := strconv.FormatInt(i+1, 10)
arena.PutDeferred(record, key, fields[i], dedupeFieldNames)
}
}
if nh > nd {
// if header longer than data: use "" values
for i = nd; i < nh; i++ {
record.PutCopy(reader.headerStrings[i], mlrval.VOID)
}
}
}
context.UpdateForInputRecord()
recordsAndContexts = append(recordsAndContexts, types.NewRecordAndContext(record, context))
}
}
return recordsAndContexts, false
}
func getRecordBatchImplicitCSVHeader(
reader *RecordReaderCSVLite,
linesChannel <-chan []string,
filename string,
context *types.Context,
errorChannel chan error,
) (
recordsAndContexts []*types.RecordAndContext,
eof bool,
) {
recordsAndContexts = []*types.RecordAndContext{}
dedupeFieldNames := reader.readerOptions.DedupeFieldNames
lines, more := <-linesChannel
if !more {
return recordsAndContexts, true
}
arena := mlrval.NewRecordArena(len(lines) * 8)
for _, line := range lines {
reader.inputLineNumber++
// Check for comments-in-data feature
// TODO: function-pointer this away
if reader.readerOptions.CommentHandling != cli.CommentsAreData {
if strings.HasPrefix(line, reader.readerOptions.CommentString) {
if reader.readerOptions.CommentHandling == cli.PassComments {
recordsAndContexts = append(recordsAndContexts, types.NewOutputString(line+"\n", context))
continue
} else if reader.readerOptions.CommentHandling == cli.SkipComments {
continue
}
// else comments are data
}
}
// This is how to do a chomp:
line = strings.TrimRight(line, reader.readerOptions.IRS)
line = strings.TrimRight(line, "\r")
if line == "" {
// Reset to new schema
reader.headerStrings = nil
continue
}
fields := reader.fieldSplitter.Split(line)
if reader.headerStrings == nil {
n := len(fields)
reader.headerStrings = make([]string, n)
for i := range n {
reader.headerStrings[i] = strconv.Itoa(i + 1)
}
} else {
if !reader.readerOptions.AllowRaggedCSVInput && len(reader.headerStrings) != len(fields) {
err := fmt.Errorf(
"CSV header/data length mismatch %d != %d at filename %s line %d",
len(reader.headerStrings), len(fields), filename, reader.inputLineNumber,
)
errorChannel <- err
return
}
}
record := mlrval.NewMlrmapAsRecord()
if !reader.readerOptions.AllowRaggedCSVInput {
for i, field := range fields {
if reader.useVoidRep && field == reader.voidRep {
field = ""
}
arena.PutDeferred(record, reader.headerStrings[i], field, dedupeFieldNames)
}
} else {
nh := int64(len(reader.headerStrings))
nd := int64(len(fields))
n := lib.IntMin2(nh, nd)
var i int64
for i = 0; i < n; i++ {
field := fields[i]
if reader.useVoidRep && field == reader.voidRep {
field = ""
}
arena.PutDeferred(record, reader.headerStrings[i], field, dedupeFieldNames)
}
if nh < nd {
// if header shorter than data: use 1-up itoa keys
for i = nh; i < nd; i++ {
field := fields[i]
if reader.useVoidRep && field == reader.voidRep {
field = ""
}
key := strconv.FormatInt(i+1, 10)
arena.PutDeferred(record, key, field, dedupeFieldNames)
}
}
if nh > nd {
// if header longer than data: use "" values
for i = nd; i < nh; i++ {
_, err := record.PutReferenceMaybeDedupe(reader.headerStrings[i], mlrval.VOID.Copy(), dedupeFieldNames)
if err != nil {
errorChannel <- err
return
}
}
}
}
context.UpdateForInputRecord()
recordsAndContexts = append(recordsAndContexts, types.NewRecordAndContext(record, context))
}
return recordsAndContexts, false
}