greenplumn plpy_typeio 源码
greenplumn plpy_typeio 代码
文件路径：/src/pl/plpython/plpy_typeio.c
/*
* transforming Datums to Python objects and vice versa
*
* src/pl/plpython/plpy_typeio.c
*/
#include "postgres.h"
#include "access/htup_details.h"
#include "catalog/pg_type.h"
#include "funcapi.h"
#include "mb/pg_wchar.h"
#include "miscadmin.h"
#include "utils/array.h"
#include "utils/builtins.h"
#include "utils/fmgroids.h"
#include "utils/lsyscache.h"
#include "utils/memutils.h"
#include "plpython.h"
#include "plpy_typeio.h"
#include "plpy_elog.h"
#include "plpy_main.h"
/* conversion from Datums to Python objects */
static PyObject *PLyBool_FromBool(PLyDatumToOb *arg, Datum d);
static PyObject *PLyFloat_FromFloat4(PLyDatumToOb *arg, Datum d);
static PyObject *PLyFloat_FromFloat8(PLyDatumToOb *arg, Datum d);
static PyObject *PLyDecimal_FromNumeric(PLyDatumToOb *arg, Datum d);
static PyObject *PLyInt_FromInt16(PLyDatumToOb *arg, Datum d);
static PyObject *PLyInt_FromInt32(PLyDatumToOb *arg, Datum d);
static PyObject *PLyLong_FromInt64(PLyDatumToOb *arg, Datum d);
static PyObject *PLyLong_FromOid(PLyDatumToOb *arg, Datum d);
static PyObject *PLyBytes_FromBytea(PLyDatumToOb *arg, Datum d);
static PyObject *PLyString_FromScalar(PLyDatumToOb *arg, Datum d);
static PyObject *PLyObject_FromTransform(PLyDatumToOb *arg, Datum d);
static PyObject *PLyList_FromArray(PLyDatumToOb *arg, Datum d);
static PyObject *PLyList_FromArray_recurse(PLyDatumToOb *elm, int *dims, int ndim, int dim,
char **dataptr_p, bits8 **bitmap_p, int *bitmask_p);
static PyObject *PLyDict_FromComposite(PLyDatumToOb *arg, Datum d);
static PyObject *PLyDict_FromTuple(PLyDatumToOb *arg, HeapTuple tuple, TupleDesc desc, bool include_generated);
/* conversion from Python objects to Datums */
static Datum PLyObject_ToBool(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray);
static Datum PLyObject_ToBytea(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray);
static Datum PLyObject_ToComposite(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray);
static Datum PLyObject_ToScalar(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray);
static Datum PLyObject_ToDomain(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray);
static Datum PLyObject_ToTransform(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray);
static Datum PLySequence_ToArray(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray);
static void PLySequence_ToArray_recurse(PLyObToDatum *elm, PyObject *list,
int *dims, int ndim, int dim,
Datum *elems, bool *nulls, int *currelem);
/* conversion from Python objects to composite Datums */
static Datum PLyString_ToComposite(PLyObToDatum *arg, PyObject *string, bool inarray);
static Datum PLyMapping_ToComposite(PLyObToDatum *arg, TupleDesc desc, PyObject *mapping);
static Datum PLySequence_ToComposite(PLyObToDatum *arg, TupleDesc desc, PyObject *sequence);
static Datum PLyGenericObject_ToComposite(PLyObToDatum *arg, TupleDesc desc, PyObject *object, bool inarray);
/*
* Conversion functions. Remember output from Python is input to
* PostgreSQL, and vice versa.
*/
/*
* Perform input conversion, given correctly-set-up state information.
*
* This is the outer-level entry point for any input conversion. Internally,
* the conversion functions recurse directly to each other.
*/
PyObject *
PLy_input_convert(PLyDatumToOb *arg, Datum val)
{
PyObject *result;
PLyExecutionContext *exec_ctx = PLy_current_execution_context();
MemoryContext scratch_context = PLy_get_scratch_context(exec_ctx);
MemoryContext oldcontext;
/*
* Do the work in the scratch context to avoid leaking memory from the
* datatype output function calls. (The individual PLyDatumToObFunc
* functions can't reset the scratch context, because they recurse and an
* inner one might clobber data an outer one still needs. So we do it
* once at the outermost recursion level.)
*
* We reset the scratch context before, not after, each conversion cycle.
* This way we aren't on the hook to release a Python refcount on the
* result object in case MemoryContextReset throws an error.
*/
MemoryContextReset(scratch_context);
oldcontext = MemoryContextSwitchTo(scratch_context);
result = arg->func(arg, val);
MemoryContextSwitchTo(oldcontext);
return result;
}
/*
* Perform output conversion, given correctly-set-up state information.
*
* This is the outer-level entry point for any output conversion. Internally,
* the conversion functions recurse directly to each other.
*
* The result, as well as any cruft generated along the way, are in the
* current memory context. Caller is responsible for cleanup.
*/
Datum
PLy_output_convert(PLyObToDatum *arg, PyObject *val, bool *isnull)
{
/* at outer level, we are not considering an array element */
return arg->func(arg, val, isnull, false);
}
/*
* Transform a tuple into a Python dict object.
*
* Note: the tupdesc must match the one used to set up *arg. We could
* insist that this function lookup the tupdesc from what is in *arg,
* but in practice all callers have the right tupdesc available.
*/
PyObject *
PLy_input_from_tuple(PLyDatumToOb *arg, HeapTuple tuple, TupleDesc desc, bool include_generated)
{
PyObject *dict;
PLyExecutionContext *exec_ctx = PLy_current_execution_context();
MemoryContext scratch_context = PLy_get_scratch_context(exec_ctx);
MemoryContext oldcontext;
/*
* As in PLy_input_convert, do the work in the scratch context.
*/
MemoryContextReset(scratch_context);
oldcontext = MemoryContextSwitchTo(scratch_context);
dict = PLyDict_FromTuple(arg, tuple, desc, include_generated);
MemoryContextSwitchTo(oldcontext);
return dict;
}
/*
* Initialize, or re-initialize, per-column input info for a composite type.
*
* This is separate from PLy_input_setup_func() because in cases involving
* anonymous record types, we need to be passed the tupdesc explicitly.
* It's caller's responsibility that the tupdesc has adequate lifespan
* in such cases. If the tupdesc is for a named composite or registered
* record type, it does not need to be long-lived.
*/
void
PLy_input_setup_tuple(PLyDatumToOb *arg, TupleDesc desc, PLyProcedure *proc)
{
int i;
/* We should be working on a previously-set-up struct */
Assert(arg->func == PLyDict_FromComposite);
/* Save pointer to tupdesc, but only if this is an anonymous record type */
if (arg->typoid == RECORDOID && arg->typmod < 0)
arg->u.tuple.recdesc = desc;
/* (Re)allocate atts array as needed */
if (arg->u.tuple.natts != desc->natts)
{
if (arg->u.tuple.atts)
pfree(arg->u.tuple.atts);
arg->u.tuple.natts = desc->natts;
arg->u.tuple.atts = (PLyDatumToOb *)
MemoryContextAllocZero(arg->mcxt,
desc->natts * sizeof(PLyDatumToOb));
}
/* Fill the atts entries, except for dropped columns */
for (i = 0; i < desc->natts; i++)
{
Form_pg_attribute attr = TupleDescAttr(desc, i);
PLyDatumToOb *att = &arg->u.tuple.atts[i];
if (attr->attisdropped)
continue;
if (att->typoid == attr->atttypid && att->typmod == attr->atttypmod)
continue; /* already set up this entry */
PLy_input_setup_func(att, arg->mcxt,
attr->atttypid, attr->atttypmod,
proc);
}
}
/*
* Initialize, or re-initialize, per-column output info for a composite type.
*
* This is separate from PLy_output_setup_func() because in cases involving
* anonymous record types, we need to be passed the tupdesc explicitly.
* It's caller's responsibility that the tupdesc has adequate lifespan
* in such cases. If the tupdesc is for a named composite or registered
* record type, it does not need to be long-lived.
*/
void
PLy_output_setup_tuple(PLyObToDatum *arg, TupleDesc desc, PLyProcedure *proc)
{
int i;
/* We should be working on a previously-set-up struct */
Assert(arg->func == PLyObject_ToComposite);
/* Save pointer to tupdesc, but only if this is an anonymous record type */
if (arg->typoid == RECORDOID && arg->typmod < 0)
arg->u.tuple.recdesc = desc;
/* (Re)allocate atts array as needed */
if (arg->u.tuple.natts != desc->natts)
{
if (arg->u.tuple.atts)
pfree(arg->u.tuple.atts);
arg->u.tuple.natts = desc->natts;
arg->u.tuple.atts = (PLyObToDatum *)
MemoryContextAllocZero(arg->mcxt,
desc->natts * sizeof(PLyObToDatum));
}
/* Fill the atts entries, except for dropped columns */
for (i = 0; i < desc->natts; i++)
{
Form_pg_attribute attr = TupleDescAttr(desc, i);
PLyObToDatum *att = &arg->u.tuple.atts[i];
if (attr->attisdropped)
continue;
if (att->typoid == attr->atttypid && att->typmod == attr->atttypmod)
continue; /* already set up this entry */
PLy_output_setup_func(att, arg->mcxt,
attr->atttypid, attr->atttypmod,
proc);
}
}
/*
* Set up output info for a PL/Python function returning record.
*
* Note: the given tupdesc is not necessarily long-lived.
*/
void
PLy_output_setup_record(PLyObToDatum *arg, TupleDesc desc, PLyProcedure *proc)
{
/* Makes no sense unless RECORD */
Assert(arg->typoid == RECORDOID);
Assert(desc->tdtypeid == RECORDOID);
/*
* Bless the record type if not already done. We'd have to do this anyway
* to return a tuple, so we might as well force the issue so we can use
* the known-record-type code path.
*/
BlessTupleDesc(desc);
/*
* Update arg->typmod, and clear the recdesc link if it's changed. The
* next call of PLyObject_ToComposite will look up a long-lived tupdesc
* for the record type.
*/
arg->typmod = desc->tdtypmod;
if (arg->u.tuple.recdesc &&
arg->u.tuple.recdesc->tdtypmod != arg->typmod)
arg->u.tuple.recdesc = NULL;
/* Update derived data if necessary */
PLy_output_setup_tuple(arg, desc, proc);
}
/*
* Recursively initialize the PLyObToDatum structure(s) needed to construct
* a SQL value of the specified typeOid/typmod from a Python value.
* (But note that at this point we may have RECORDOID/-1, ie, an indeterminate
* record type.)
* proc is used to look up transform functions.
*/
void
PLy_output_setup_func(PLyObToDatum *arg, MemoryContext arg_mcxt,
Oid typeOid, int32 typmod,
PLyProcedure *proc)
{
TypeCacheEntry *typentry;
char typtype;
Oid trfuncid;
Oid typinput;
/* Since this is recursive, it could theoretically be driven to overflow */
check_stack_depth();
arg->typoid = typeOid;
arg->typmod = typmod;
arg->mcxt = arg_mcxt;
/*
* Fetch typcache entry for the target type, asking for whatever info
* we'll need later. RECORD is a special case: just treat it as composite
* without bothering with the typcache entry.
*/
if (typeOid != RECORDOID)
{
typentry = lookup_type_cache(typeOid, TYPECACHE_DOMAIN_BASE_INFO);
typtype = typentry->typtype;
arg->typbyval = typentry->typbyval;
arg->typlen = typentry->typlen;
arg->typalign = typentry->typalign;
}
else
{
typentry = NULL;
typtype = TYPTYPE_COMPOSITE;
/* hard-wired knowledge about type RECORD: */
arg->typbyval = false;
arg->typlen = -1;
arg->typalign = 'd';
}
/*
* Choose conversion method. Note that transform functions are checked
* for composite and scalar types, but not for arrays or domains. This is
* somewhat historical, but we'd have a problem allowing them on domains,
* since we drill down through all levels of a domain nest without looking
* at the intermediate levels at all.
*/
if (typtype == TYPTYPE_DOMAIN)
{
/* Domain */
arg->func = PLyObject_ToDomain;
arg->u.domain.domain_info = NULL;
/* Recursively set up conversion info for the element type */
arg->u.domain.base = (PLyObToDatum *)
MemoryContextAllocZero(arg_mcxt, sizeof(PLyObToDatum));
PLy_output_setup_func(arg->u.domain.base, arg_mcxt,
typentry->domainBaseType,
typentry->domainBaseTypmod,
proc);
}
else if (typentry &&
OidIsValid(typentry->typelem) && typentry->typlen == -1)
{
/* Standard varlena array (cf. get_element_type) */
arg->func = PLySequence_ToArray;
/* Get base type OID to insert into constructed array */
/* (note this might not be the same as the immediate child type) */
arg->u.array.elmbasetype = getBaseType(typentry->typelem);
/* Recursively set up conversion info for the element type */
arg->u.array.elm = (PLyObToDatum *)
MemoryContextAllocZero(arg_mcxt, sizeof(PLyObToDatum));
PLy_output_setup_func(arg->u.array.elm, arg_mcxt,
typentry->typelem, typmod,
proc);
}
else if ((trfuncid = get_transform_tosql(typeOid,
proc->langid,
proc->trftypes)))
{
arg->func = PLyObject_ToTransform;
fmgr_info_cxt(trfuncid, &arg->u.transform.typtransform, arg_mcxt);
}
else if (typtype == TYPTYPE_COMPOSITE)
{
/* Named composite type, or RECORD */
arg->func = PLyObject_ToComposite;
/* We'll set up the per-field data later */
arg->u.tuple.recdesc = NULL;
arg->u.tuple.typentry = typentry;
arg->u.tuple.tupdescid = INVALID_TUPLEDESC_IDENTIFIER;
arg->u.tuple.atts = NULL;
arg->u.tuple.natts = 0;
/* Mark this invalid till needed, too */
arg->u.tuple.recinfunc.fn_oid = InvalidOid;
}
else
{
/* Scalar type, but we have a couple of special cases */
switch (typeOid)
{
case BOOLOID:
arg->func = PLyObject_ToBool;
break;
case BYTEAOID:
arg->func = PLyObject_ToBytea;
break;
default:
arg->func = PLyObject_ToScalar;
getTypeInputInfo(typeOid, &typinput, &arg->u.scalar.typioparam);
fmgr_info_cxt(typinput, &arg->u.scalar.typfunc, arg_mcxt);
break;
}
}
}
/*
* Recursively initialize the PLyDatumToOb structure(s) needed to construct
* a Python value from a SQL value of the specified typeOid/typmod.
* (But note that at this point we may have RECORDOID/-1, ie, an indeterminate
* record type.)
* proc is used to look up transform functions.
*/
void
PLy_input_setup_func(PLyDatumToOb *arg, MemoryContext arg_mcxt,
Oid typeOid, int32 typmod,
PLyProcedure *proc)
{
TypeCacheEntry *typentry;
char typtype;
Oid trfuncid;
Oid typoutput;
bool typisvarlena;
/* Since this is recursive, it could theoretically be driven to overflow */
check_stack_depth();
arg->typoid = typeOid;
arg->typmod = typmod;
arg->mcxt = arg_mcxt;
/*
* Fetch typcache entry for the target type, asking for whatever info
* we'll need later. RECORD is a special case: just treat it as composite
* without bothering with the typcache entry.
*/
if (typeOid != RECORDOID)
{
typentry = lookup_type_cache(typeOid, TYPECACHE_DOMAIN_BASE_INFO);
typtype = typentry->typtype;
arg->typbyval = typentry->typbyval;
arg->typlen = typentry->typlen;
arg->typalign = typentry->typalign;
}
else
{
typentry = NULL;
typtype = TYPTYPE_COMPOSITE;
/* hard-wired knowledge about type RECORD: */
arg->typbyval = false;
arg->typlen = -1;
arg->typalign = 'd';
}
/*
* Choose conversion method. Note that transform functions are checked
* for composite and scalar types, but not for arrays or domains. This is
* somewhat historical, but we'd have a problem allowing them on domains,
* since we drill down through all levels of a domain nest without looking
* at the intermediate levels at all.
*/
if (typtype == TYPTYPE_DOMAIN)
{
/* Domain --- we don't care, just recurse down to the base type */
PLy_input_setup_func(arg, arg_mcxt,
typentry->domainBaseType,
typentry->domainBaseTypmod,
proc);
}
else if (typentry &&
OidIsValid(typentry->typelem) && typentry->typlen == -1)
{
/* Standard varlena array (cf. get_element_type) */
arg->func = PLyList_FromArray;
/* Recursively set up conversion info for the element type */
arg->u.array.elm = (PLyDatumToOb *)
MemoryContextAllocZero(arg_mcxt, sizeof(PLyDatumToOb));
PLy_input_setup_func(arg->u.array.elm, arg_mcxt,
typentry->typelem, typmod,
proc);
}
else if ((trfuncid = get_transform_fromsql(typeOid,
proc->langid,
proc->trftypes)))
{
arg->func = PLyObject_FromTransform;
fmgr_info_cxt(trfuncid, &arg->u.transform.typtransform, arg_mcxt);
}
else if (typtype == TYPTYPE_COMPOSITE)
{
/* Named composite type, or RECORD */
arg->func = PLyDict_FromComposite;
/* We'll set up the per-field data later */
arg->u.tuple.recdesc = NULL;
arg->u.tuple.typentry = typentry;
arg->u.tuple.tupdescid = INVALID_TUPLEDESC_IDENTIFIER;
arg->u.tuple.atts = NULL;
arg->u.tuple.natts = 0;
}
else
{
/* Scalar type, but we have a couple of special cases */
switch (typeOid)
{
case BOOLOID:
arg->func = PLyBool_FromBool;
break;
case FLOAT4OID:
arg->func = PLyFloat_FromFloat4;
break;
case FLOAT8OID:
arg->func = PLyFloat_FromFloat8;
break;
case NUMERICOID:
arg->func = PLyDecimal_FromNumeric;
break;
case INT2OID:
arg->func = PLyInt_FromInt16;
break;
case INT4OID:
arg->func = PLyInt_FromInt32;
break;
case INT8OID:
arg->func = PLyLong_FromInt64;
break;
case OIDOID:
arg->func = PLyLong_FromOid;
break;
case BYTEAOID:
arg->func = PLyBytes_FromBytea;
break;
default:
arg->func = PLyString_FromScalar;
getTypeOutputInfo(typeOid, &typoutput, &typisvarlena);
fmgr_info_cxt(typoutput, &arg->u.scalar.typfunc, arg_mcxt);
break;
}
}
}
/*
* Special-purpose input converters.
*/
static PyObject *
PLyBool_FromBool(PLyDatumToOb *arg, Datum d)
{
if (DatumGetBool(d))
Py_RETURN_TRUE;
Py_RETURN_FALSE;
}
static PyObject *
PLyFloat_FromFloat4(PLyDatumToOb *arg, Datum d)
{
return PyFloat_FromDouble(DatumGetFloat4(d));
}
static PyObject *
PLyFloat_FromFloat8(PLyDatumToOb *arg, Datum d)
{
return PyFloat_FromDouble(DatumGetFloat8(d));
}
static PyObject *
PLyDecimal_FromNumeric(PLyDatumToOb *arg, Datum d)
{
static PyObject *decimal_constructor;
char *str;
PyObject *pyvalue;
/* Try to import cdecimal. If it doesn't exist, fall back to decimal. */
if (!decimal_constructor)
{
PyObject *decimal_module;
decimal_module = PyImport_ImportModule("cdecimal");
if (!decimal_module)
{
PyErr_Clear();
decimal_module = PyImport_ImportModule("decimal");
}
if (!decimal_module)
PLy_elog(ERROR, "could not import a module for Decimal constructor");
decimal_constructor = PyObject_GetAttrString(decimal_module, "Decimal");
if (!decimal_constructor)
PLy_elog(ERROR, "no Decimal attribute in module");
}
str = DatumGetCString(DirectFunctionCall1(numeric_out, d));
pyvalue = PyObject_CallFunction(decimal_constructor, "s", str);
if (!pyvalue)
PLy_elog(ERROR, "conversion from numeric to Decimal failed");
return pyvalue;
}
static PyObject *
PLyInt_FromInt16(PLyDatumToOb *arg, Datum d)
{
return PyInt_FromLong(DatumGetInt16(d));
}
static PyObject *
PLyInt_FromInt32(PLyDatumToOb *arg, Datum d)
{
return PyInt_FromLong(DatumGetInt32(d));
}
static PyObject *
PLyLong_FromInt64(PLyDatumToOb *arg, Datum d)
{
return PyLong_FromLongLong(DatumGetInt64(d));
}
static PyObject *
PLyLong_FromOid(PLyDatumToOb *arg, Datum d)
{
return PyLong_FromUnsignedLong(DatumGetObjectId(d));
}
static PyObject *
PLyBytes_FromBytea(PLyDatumToOb *arg, Datum d)
{
text *txt = DatumGetByteaPP(d);
char *str = VARDATA_ANY(txt);
size_t size = VARSIZE_ANY_EXHDR(txt);
return PyBytes_FromStringAndSize(str, size);
}
/*
* Generic input conversion using a SQL type's output function.
*/
static PyObject *
PLyString_FromScalar(PLyDatumToOb *arg, Datum d)
{
char *x = OutputFunctionCall(&arg->u.scalar.typfunc, d);
PyObject *r = PyString_FromString(x);
pfree(x);
return r;
}
/*
* Convert using a from-SQL transform function.
*/
static PyObject *
PLyObject_FromTransform(PLyDatumToOb *arg, Datum d)
{
Datum t;
t = FunctionCall1(&arg->u.transform.typtransform, d);
return (PyObject *) DatumGetPointer(t);
}
/*
* Convert a SQL array to a Python list.
*/
static PyObject *
PLyList_FromArray(PLyDatumToOb *arg, Datum d)
{
ArrayType *array = DatumGetArrayTypeP(d);
PLyDatumToOb *elm = arg->u.array.elm;
int ndim;
int *dims;
char *dataptr;
bits8 *bitmap;
int bitmask;
if (ARR_NDIM(array) == 0)
return PyList_New(0);
/* Array dimensions and left bounds */
ndim = ARR_NDIM(array);
dims = ARR_DIMS(array);
Assert(ndim < MAXDIM);
/*
* We iterate the SQL array in the physical order it's stored in the
* datum. For example, for a 3-dimensional array the order of iteration
* would be the following: [0,0,0] elements through [0,0,k], then [0,1,0]
* through [0,1,k] till [0,m,k], then [1,0,0] through [1,0,k] till
* [1,m,k], and so on.
*
* In Python, there are no multi-dimensional lists as such, but they are
* represented as a list of lists. So a 3-d array of [n,m,k] elements is a
* list of n m-element arrays, each element of which is k-element array.
* PLyList_FromArray_recurse() builds the Python list for a single
* dimension, and recurses for the next inner dimension.
*/
dataptr = ARR_DATA_PTR(array);
bitmap = ARR_NULLBITMAP(array);
bitmask = 1;
return PLyList_FromArray_recurse(elm, dims, ndim, 0,
&dataptr, &bitmap, &bitmask);
}
static PyObject *
PLyList_FromArray_recurse(PLyDatumToOb *elm, int *dims, int ndim, int dim,
char **dataptr_p, bits8 **bitmap_p, int *bitmask_p)
{
int i;
PyObject *list;
list = PyList_New(dims[dim]);
if (!list)
return NULL;
if (dim < ndim - 1)
{
/* Outer dimension. Recurse for each inner slice. */
for (i = 0; i < dims[dim]; i++)
{
PyObject *sublist;
sublist = PLyList_FromArray_recurse(elm, dims, ndim, dim + 1,
dataptr_p, bitmap_p, bitmask_p);
PyList_SET_ITEM(list, i, sublist);
}
}
else
{
/*
* Innermost dimension. Fill the list with the values from the array
* for this slice.
*/
char *dataptr = *dataptr_p;
bits8 *bitmap = *bitmap_p;
int bitmask = *bitmask_p;
for (i = 0; i < dims[dim]; i++)
{
/* checking for NULL */
if (bitmap && (*bitmap & bitmask) == 0)
{
Py_INCREF(Py_None);
PyList_SET_ITEM(list, i, Py_None);
}
else
{
Datum itemvalue;
itemvalue = fetch_att(dataptr, elm->typbyval, elm->typlen);
PyList_SET_ITEM(list, i, elm->func(elm, itemvalue));
dataptr = att_addlength_pointer(dataptr, elm->typlen, dataptr);
dataptr = (char *) att_align_nominal(dataptr, elm->typalign);
}
/* advance bitmap pointer if any */
if (bitmap)
{
bitmask <<= 1;
if (bitmask == 0x100 /* (1<<8) */ )
{
bitmap++;
bitmask = 1;
}
}
}
*dataptr_p = dataptr;
*bitmap_p = bitmap;
*bitmask_p = bitmask;
}
return list;
}
/*
* Convert a composite SQL value to a Python dict.
*/
static PyObject *
PLyDict_FromComposite(PLyDatumToOb *arg, Datum d)
{
PyObject *dict;
HeapTupleHeader td;
Oid tupType;
int32 tupTypmod;
TupleDesc tupdesc;
HeapTupleData tmptup;
td = DatumGetHeapTupleHeader(d);
/* Extract rowtype info and find a tupdesc */
tupType = HeapTupleHeaderGetTypeId(td);
tupTypmod = HeapTupleHeaderGetTypMod(td);
tupdesc = lookup_rowtype_tupdesc(tupType, tupTypmod);
/* Set up I/O funcs if not done yet */
PLy_input_setup_tuple(arg, tupdesc,
PLy_current_execution_context()->curr_proc);
/* Build a temporary HeapTuple control structure */
tmptup.t_len = HeapTupleHeaderGetDatumLength(td);
tmptup.t_data = td;
dict = PLyDict_FromTuple(arg, &tmptup, tupdesc, true);
ReleaseTupleDesc(tupdesc);
return dict;
}
/*
* Transform a tuple into a Python dict object.
*/
static PyObject *
PLyDict_FromTuple(PLyDatumToOb *arg, HeapTuple tuple, TupleDesc desc, bool include_generated)
{
PyObject *volatile dict;
/* Simple sanity check that desc matches */
Assert(desc->natts == arg->u.tuple.natts);
dict = PyDict_New();
if (dict == NULL)
return NULL;
PG_TRY();
{
int i;
for (i = 0; i < arg->u.tuple.natts; i++)
{
PLyDatumToOb *att = &arg->u.tuple.atts[i];
Form_pg_attribute attr = TupleDescAttr(desc, i);
char *key;
Datum vattr;
bool is_null;
PyObject *value;
if (attr->attisdropped)
continue;
if (attr->attgenerated)
{
/* don't include unless requested */
if (!include_generated)
continue;
}
key = NameStr(attr->attname);
vattr = heap_getattr(tuple, (i + 1), desc, &is_null);
if (is_null)
PyDict_SetItemString(dict, key, Py_None);
else
{
value = att->func(att, vattr);
PyDict_SetItemString(dict, key, value);
Py_DECREF(value);
}
}
}
PG_CATCH();
{
Py_DECREF(dict);
PG_RE_THROW();
}
PG_END_TRY();
return dict;
}
/*
* Convert a Python object to a PostgreSQL bool datum. This can't go
* through the generic conversion function, because Python attaches a
* Boolean value to everything, more things than the PostgreSQL bool
* type can parse.
*/
static Datum
PLyObject_ToBool(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray)
{
if (plrv == Py_None)
{
*isnull = true;
return (Datum) 0;
}
*isnull = false;
return BoolGetDatum(PyObject_IsTrue(plrv));
}
/*
* Convert a Python object to a PostgreSQL bytea datum. This doesn't
* go through the generic conversion function to circumvent problems
* with embedded nulls. And it's faster this way.
*/
static Datum
PLyObject_ToBytea(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray)
{
PyObject *volatile plrv_so = NULL;
Datum rv;
if (plrv == Py_None)
{
*isnull = true;
return (Datum) 0;
}
*isnull = false;
plrv_so = PyObject_Bytes(plrv);
if (!plrv_so)
PLy_elog(ERROR, "could not create bytes representation of Python object");
PG_TRY();
{
char *plrv_sc = PyBytes_AsString(plrv_so);
size_t len = PyBytes_Size(plrv_so);
size_t size = len + VARHDRSZ;
bytea *result = palloc(size);
SET_VARSIZE(result, size);
memcpy(VARDATA(result), plrv_sc, len);
rv = PointerGetDatum(result);
}
PG_CATCH();
{
Py_XDECREF(plrv_so);
PG_RE_THROW();
}
PG_END_TRY();
Py_XDECREF(plrv_so);
return rv;
}
/*
* Convert a Python object to a composite type. First look up the type's
* description, then route the Python object through the conversion function
* for obtaining PostgreSQL tuples.
*/
static Datum
PLyObject_ToComposite(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray)
{
Datum rv;
TupleDesc desc;
if (plrv == Py_None)
{
*isnull = true;
return (Datum) 0;
}
*isnull = false;
/*
* The string conversion case doesn't require a tupdesc, nor per-field
* conversion data, so just go for it if that's the case to use.
*/
if (PyString_Check(plrv) || PyUnicode_Check(plrv))
return PLyString_ToComposite(arg, plrv, inarray);
/*
* If we're dealing with a named composite type, we must look up the
* tupdesc every time, to protect against possible changes to the type.
* RECORD types can't change between calls; but we must still be willing
* to set up the info the first time, if nobody did yet.
*/
if (arg->typoid != RECORDOID)
{
desc = lookup_rowtype_tupdesc(arg->typoid, arg->typmod);
/* We should have the descriptor of the type's typcache entry */
Assert(desc == arg->u.tuple.typentry->tupDesc);
/* Detect change of descriptor, update cache if needed */
if (arg->u.tuple.tupdescid != arg->u.tuple.typentry->tupDesc_identifier)
{
PLy_output_setup_tuple(arg, desc,
PLy_current_execution_context()->curr_proc);
arg->u.tuple.tupdescid = arg->u.tuple.typentry->tupDesc_identifier;
}
}
else
{
desc = arg->u.tuple.recdesc;
if (desc == NULL)
{
desc = lookup_rowtype_tupdesc(arg->typoid, arg->typmod);
arg->u.tuple.recdesc = desc;
}
else
{
/* Pin descriptor to match unpin below */
PinTupleDesc(desc);
}
}
/* Simple sanity check on our caching */
Assert(desc->natts == arg->u.tuple.natts);
/*
* Convert, using the appropriate method depending on the type of the
* supplied Python object.
*/
if (PySequence_Check(plrv))
/* composite type as sequence (tuple, list etc) */
rv = PLySequence_ToComposite(arg, desc, plrv);
else if (PyMapping_Check(plrv))
/* composite type as mapping (currently only dict) */
rv = PLyMapping_ToComposite(arg, desc, plrv);
else
/* returned as smth, must provide method __getattr__(name) */
rv = PLyGenericObject_ToComposite(arg, desc, plrv, inarray);
ReleaseTupleDesc(desc);
return rv;
}
/*
* Convert Python object to C string in server encoding.
*
* Note: this is exported for use by add-on transform modules.
*/
char *
PLyObject_AsString(PyObject *plrv)
{
PyObject *plrv_bo;
char *plrv_sc;
size_t plen;
size_t slen;
if (PyUnicode_Check(plrv))
plrv_bo = PLyUnicode_Bytes(plrv);
else if (PyFloat_Check(plrv))
{
/* use repr() for floats, str() is lossy */
#if PY_MAJOR_VERSION >= 3
PyObject *s = PyObject_Repr(plrv);
plrv_bo = PLyUnicode_Bytes(s);
Py_XDECREF(s);
#else
plrv_bo = PyObject_Repr(plrv);
#endif
}
else
{
#if PY_MAJOR_VERSION >= 3
PyObject *s = PyObject_Str(plrv);
plrv_bo = PLyUnicode_Bytes(s);
Py_XDECREF(s);
#else
plrv_bo = PyObject_Str(plrv);
#endif
}
if (!plrv_bo)
PLy_elog(ERROR, "could not create string representation of Python object");
plrv_sc = pstrdup(PyBytes_AsString(plrv_bo));
plen = PyBytes_Size(plrv_bo);
slen = strlen(plrv_sc);
Py_XDECREF(plrv_bo);
if (slen < plen)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("could not convert Python object into cstring: Python string representation appears to contain null bytes")));
else if (slen > plen)
elog(ERROR, "could not convert Python object into cstring: Python string longer than reported length");
pg_verifymbstr(plrv_sc, slen, false);
return plrv_sc;
}
/*
* Generic output conversion function: convert PyObject to cstring and
* cstring into PostgreSQL type.
*/
static Datum
PLyObject_ToScalar(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray)
{
char *str;
if (plrv == Py_None)
{
*isnull = true;
return (Datum) 0;
}
*isnull = false;
str = PLyObject_AsString(plrv);
return InputFunctionCall(&arg->u.scalar.typfunc,
str,
arg->u.scalar.typioparam,
arg->typmod);
}
/*
* Convert to a domain type.
*/
static Datum
PLyObject_ToDomain(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray)
{
Datum result;
PLyObToDatum *base = arg->u.domain.base;
result = base->func(base, plrv, isnull, inarray);
domain_check(result, *isnull, arg->typoid,
&arg->u.domain.domain_info, arg->mcxt);
return result;
}
/*
* Convert using a to-SQL transform function.
*/
static Datum
PLyObject_ToTransform(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray)
{
if (plrv == Py_None)
{
*isnull = true;
return (Datum) 0;
}
*isnull = false;
return FunctionCall1(&arg->u.transform.typtransform, PointerGetDatum(plrv));
}
/*
* Convert Python sequence to SQL array.
*/
static Datum
PLySequence_ToArray(PLyObToDatum *arg, PyObject *plrv,
bool *isnull, bool inarray)
{
ArrayType *array;
int i;
Datum *elems;
bool *nulls;
int64 len;
int ndim;
int dims[MAXDIM];
int lbs[MAXDIM];
int currelem;
PyObject *pyptr = plrv;
PyObject *next;
if (plrv == Py_None)
{
*isnull = true;
return (Datum) 0;
}
*isnull = false;
/*
* Determine the number of dimensions, and their sizes.
*/
ndim = 0;
len = 1;
Py_INCREF(plrv);
for (;;)
{
if (!PyList_Check(pyptr))
break;
if (ndim == MAXDIM)
ereport(ERROR,
(errcode(ERRCODE_PROGRAM_LIMIT_EXCEEDED),
errmsg("number of array dimensions exceeds the maximum allowed (%d)",
MAXDIM)));
dims[ndim] = PySequence_Length(pyptr);
if (dims[ndim] < 0)
PLy_elog(ERROR, "could not determine sequence length for function return value");
if (dims[ndim] > MaxAllocSize)
ereport(ERROR,
(errcode(ERRCODE_PROGRAM_LIMIT_EXCEEDED),
errmsg("array size exceeds the maximum allowed")));
len *= dims[ndim];
if (len > MaxAllocSize)
ereport(ERROR,
(errcode(ERRCODE_PROGRAM_LIMIT_EXCEEDED),
errmsg("array size exceeds the maximum allowed")));
if (dims[ndim] == 0)
{
/* empty sequence */
break;
}
ndim++;
next = PySequence_GetItem(pyptr, 0);
Py_XDECREF(pyptr);
pyptr = next;
}
Py_XDECREF(pyptr);
/*
* Check for zero dimensions. This happens if the object is a tuple or a
* string, rather than a list, or is not a sequence at all. We don't map
* tuples or strings to arrays in general, but in the first level, be
* lenient, for historical reasons. So if the object is a sequence of any
* kind, treat it as a one-dimensional array.
*/
if (ndim == 0)
{
if (!PySequence_Check(plrv))
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("return value of function with array return type is not a Python sequence")));
ndim = 1;
len = dims[0] = PySequence_Length(plrv);
}
/*
* Traverse the Python lists, in depth-first order, and collect all the
* elements at the bottom level into 'elems'/'nulls' arrays.
*/
elems = palloc(sizeof(Datum) * len);
nulls = palloc(sizeof(bool) * len);
currelem = 0;
PLySequence_ToArray_recurse(arg->u.array.elm, plrv,
dims, ndim, 0,
elems, nulls, &currelem);
for (i = 0; i < ndim; i++)
lbs[i] = 1;
array = construct_md_array(elems,
nulls,
ndim,
dims,
lbs,
arg->u.array.elmbasetype,
arg->u.array.elm->typlen,
arg->u.array.elm->typbyval,
arg->u.array.elm->typalign);
return PointerGetDatum(array);
}
/*
* Helper function for PLySequence_ToArray. Traverse a Python list of lists in
* depth-first order, storing the elements in 'elems'.
*/
static void
PLySequence_ToArray_recurse(PLyObToDatum *elm, PyObject *list,
int *dims, int ndim, int dim,
Datum *elems, bool *nulls, int *currelem)
{
int i;
if (PySequence_Length(list) != dims[dim])
ereport(ERROR,
(errcode(ERRCODE_ARRAY_SUBSCRIPT_ERROR),
errmsg("wrong length of inner sequence: has length %d, but %d was expected",
(int) PySequence_Length(list), dims[dim]),
(errdetail("To construct a multidimensional array, the inner sequences must all have the same length."))));
if (dim < ndim - 1)
{
for (i = 0; i < dims[dim]; i++)
{
PyObject *sublist = PySequence_GetItem(list, i);
PLySequence_ToArray_recurse(elm, sublist, dims, ndim, dim + 1,
elems, nulls, currelem);
Py_XDECREF(sublist);
}
}
else
{
for (i = 0; i < dims[dim]; i++)
{
PyObject *obj = PySequence_GetItem(list, i);
elems[*currelem] = elm->func(elm, obj, &nulls[*currelem], true);
Py_XDECREF(obj);
(*currelem)++;
}
}
}
/*
* Convert a Python string to composite, using record_in.
*/
static Datum
PLyString_ToComposite(PLyObToDatum *arg, PyObject *string, bool inarray)
{
char *str;
/*
* Set up call data for record_in, if we didn't already. (We can't just
* use DirectFunctionCall, because record_in needs a fn_extra field.)
*/
if (!OidIsValid(arg->u.tuple.recinfunc.fn_oid))
fmgr_info_cxt(F_RECORD_IN, &arg->u.tuple.recinfunc, arg->mcxt);
str = PLyObject_AsString(string);
/*
* If we are parsing a composite type within an array, and the string
* isn't a valid record literal, there's a high chance that the function
* did something like:
*
* CREATE FUNCTION .. RETURNS comptype[] AS $$ return [['foo', 'bar']] $$
* LANGUAGE plpython;
*
* Before PostgreSQL 10, that was interpreted as a single-dimensional
* array, containing record ('foo', 'bar'). PostgreSQL 10 added support
* for multi-dimensional arrays, and it is now interpreted as a
* two-dimensional array, containing two records, 'foo', and 'bar'.
* record_in() will throw an error, because "foo" is not a valid record
* literal.
*
* To make that less confusing to users who are upgrading from older
* versions, try to give a hint in the typical instances of that. If we
* are parsing an array of composite types, and we see a string literal
* that is not a valid record literal, give a hint. We only want to give
* the hint in the narrow case of a malformed string literal, not any
* error from record_in(), so check for that case here specifically.
*
* This check better match the one in record_in(), so that we don't forbid
* literals that are actually valid!
*/
if (inarray)
{
char *ptr = str;
/* Allow leading whitespace */
while (*ptr && isspace((unsigned char) *ptr))
ptr++;
if (*ptr++ != '(')
ereport(ERROR,
(errcode(ERRCODE_INVALID_TEXT_REPRESENTATION),
errmsg("malformed record literal: \"%s\"", str),
errdetail("Missing left parenthesis."),
errhint("To return a composite type in an array, return the composite type as a Python tuple, e.g., \"[('foo',)]\".")));
}
return InputFunctionCall(&arg->u.tuple.recinfunc,
str,
arg->typoid,
arg->typmod);
}
static Datum
PLyMapping_ToComposite(PLyObToDatum *arg, TupleDesc desc, PyObject *mapping)
{
Datum result;
HeapTuple tuple;
Datum *values;
bool *nulls;
volatile int i;
Assert(PyMapping_Check(mapping));
/* Build tuple */
values = palloc(sizeof(Datum) * desc->natts);
nulls = palloc(sizeof(bool) * desc->natts);
for (i = 0; i < desc->natts; ++i)
{
char *key;
PyObject *volatile value;
PLyObToDatum *att;
Form_pg_attribute attr = TupleDescAttr(desc, i);
if (attr->attisdropped)
{
values[i] = (Datum) 0;
nulls[i] = true;
continue;
}
key = NameStr(attr->attname);
value = NULL;
att = &arg->u.tuple.atts[i];
PG_TRY();
{
value = PyMapping_GetItemString(mapping, key);
if (!value)
ereport(ERROR,
(errcode(ERRCODE_UNDEFINED_COLUMN),
errmsg("key \"%s\" not found in mapping", key),
errhint("To return null in a column, "
"add the value None to the mapping with the key named after the column.")));
values[i] = att->func(att, value, &nulls[i], false);
Py_XDECREF(value);
value = NULL;
}
PG_CATCH();
{
Py_XDECREF(value);
PG_RE_THROW();
}
PG_END_TRY();
}
tuple = heap_form_tuple(desc, values, nulls);
result = heap_copy_tuple_as_datum(tuple, desc);
heap_freetuple(tuple);
pfree(values);
pfree(nulls);
return result;
}
static Datum
PLySequence_ToComposite(PLyObToDatum *arg, TupleDesc desc, PyObject *sequence)
{
Datum result;
HeapTuple tuple;
Datum *values;
bool *nulls;
volatile int idx;
volatile int i;
Assert(PySequence_Check(sequence));
/*
* Check that sequence length is exactly same as PG tuple's. We actually
* can ignore exceeding items or assume missing ones as null but to avoid
* plpython developer's errors we are strict here
*/
idx = 0;
for (i = 0; i < desc->natts; i++)
{
if (!TupleDescAttr(desc, i)->attisdropped)
idx++;
}
if (PySequence_Length(sequence) != idx)
ereport(ERROR,
(errcode(ERRCODE_DATATYPE_MISMATCH),
errmsg("length of returned sequence did not match number of columns in row")));
/* Build tuple */
values = palloc(sizeof(Datum) * desc->natts);
nulls = palloc(sizeof(bool) * desc->natts);
idx = 0;
for (i = 0; i < desc->natts; ++i)
{
PyObject *volatile value;
PLyObToDatum *att;
if (TupleDescAttr(desc, i)->attisdropped)
{
values[i] = (Datum) 0;
nulls[i] = true;
continue;
}
value = NULL;
att = &arg->u.tuple.atts[i];
PG_TRY();
{
value = PySequence_GetItem(sequence, idx);
Assert(value);
values[i] = att->func(att, value, &nulls[i], false);
Py_XDECREF(value);
value = NULL;
}
PG_CATCH();
{
Py_XDECREF(value);
PG_RE_THROW();
}
PG_END_TRY();
idx++;
}
tuple = heap_form_tuple(desc, values, nulls);
result = heap_copy_tuple_as_datum(tuple, desc);
heap_freetuple(tuple);
pfree(values);
pfree(nulls);
return result;
}
static Datum
PLyGenericObject_ToComposite(PLyObToDatum *arg, TupleDesc desc, PyObject *object, bool inarray)
{
Datum result;
HeapTuple tuple;
Datum *values;
bool *nulls;
volatile int i;
/* Build tuple */
values = palloc(sizeof(Datum) * desc->natts);
nulls = palloc(sizeof(bool) * desc->natts);
for (i = 0; i < desc->natts; ++i)
{
char *key;
PyObject *volatile value;
PLyObToDatum *att;
Form_pg_attribute attr = TupleDescAttr(desc, i);
if (attr->attisdropped)
{
values[i] = (Datum) 0;
nulls[i] = true;
continue;
}
key = NameStr(attr->attname);
value = NULL;
att = &arg->u.tuple.atts[i];
PG_TRY();
{
value = PyObject_GetAttrString(object, key);
if (!value)
{
/*
* No attribute for this column in the object.
*
* If we are parsing a composite type in an array, a likely
* cause is that the function contained something like "[[123,
* 'foo']]". Before PostgreSQL 10, that was interpreted as an
* array, with a composite type (123, 'foo') in it. But now
* it's interpreted as a two-dimensional array, and we try to
* interpret "123" as the composite type. See also similar
* heuristic in PLyObject_ToScalar().
*/
ereport(ERROR,
(errcode(ERRCODE_UNDEFINED_COLUMN),
errmsg("attribute \"%s\" does not exist in Python object", key),
inarray ?
errhint("To return a composite type in an array, return the composite type as a Python tuple, e.g., \"[('foo',)]\".") :
errhint("To return null in a column, let the returned object have an attribute named after column with value None.")));
}
values[i] = att->func(att, value, &nulls[i], false);
Py_XDECREF(value);
value = NULL;
}
PG_CATCH();
{
Py_XDECREF(value);
PG_RE_THROW();
}
PG_END_TRY();
}
tuple = heap_form_tuple(desc, values, nulls);
result = heap_copy_tuple_as_datum(tuple, desc);
heap_freetuple(tuple);
pfree(values);
pfree(nulls);
return result;
}
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