[{"data":1,"prerenderedAt":957},["ShallowReactive",2],{"page-\u002Fadvanced-query-patterns-and-bulk-data-operations\u002Fhigh-performance-bulk-inserts-and-updates\u002Fbatch-inserting-millions-of-rows-with-sqlalchemy-coreexecute\u002F":3},{"id":4,"title":5,"body":6,"description":950,"extension":951,"meta":952,"navigation":122,"path":953,"seo":954,"stem":955,"__hash__":956},"content\u002Fadvanced-query-patterns-and-bulk-data-operations\u002Fhigh-performance-bulk-inserts-and-updates\u002Fbatch-inserting-millions-of-rows-with-sqlalchemy-coreexecute\u002Findex.md","Batch Inserting Millions of Rows with SQLAlchemy core.execute",{"type":7,"value":8,"toc":938},"minimark",[9,18,52,57,76,81,235,239,491,495,523,527,710,714,800,804,872,876,889,895,909,934],[10,11,13,14],"h1",{"id":12},"batch-inserting-millions-of-rows-with-sqlalchemy-coreexecute","Batch Inserting Millions of Rows with SQLAlchemy ",[15,16,17],"code",{},"core.execute",[19,20,21,22,25,26,29,30,33,34,37,38,41,42,45,46,51],"p",{},"To batch insert millions of rows using SQLAlchemy 2.0 Core, initialize an ",[15,23,24],{},"AsyncEngine"," with ",[15,27,28],{},"create_async_engine()",", wrap operations in an ",[15,31,32],{},"async with engine.connect()"," transaction block, and pass chunked dictionaries directly to ",[15,35,36],{},"await conn.execute(stmt, chunk)",". SQLAlchemy 2.0 automatically detects sequences of dictionaries and delegates execution to the underlying DBAPI's ",[15,39,40],{},"executemany"," implementation. Always call ",[15,43,44],{},"await conn.commit()"," explicitly before the context manager exits to prevent silent rollbacks. This execution model aligns with established ",[47,48,50],"a",{"href":49},"\u002Fadvanced-query-patterns-and-bulk-data-operations\u002F","Advanced Query Patterns and Bulk Data Operations"," for scalable, production-grade data pipelines.",[53,54,56],"h2",{"id":55},"step-by-step-memory-safe-chunking-async-execution","Step-by-Step: Memory-Safe Chunking & Async Execution",[19,58,59,60,63,64,67,68,71,72,75],{},"Accumulating millions of rows in Python lists triggers immediate ",[15,61,62],{},"MemoryError"," exceptions. Replace list accumulation with generator functions that yield row dictionaries on-demand. Use ",[15,65,66],{},"itertools.islice()"," to materialize fixed-size chunks (10,000–50,000 rows) per iteration. Wrap the execution loop in ",[15,69,70],{},"try\u002Fexcept"," to guarantee transactional integrity, and disable result fetching via ",[15,73,74],{},"execution_options(no_returning=True)"," to prevent RAM exhaustion from generated primary keys.",[77,78,80],"h3",{"id":79},"memory-safe-data-streaming-pattern","Memory-Safe Data Streaming Pattern",[82,83,88],"pre",{"className":84,"code":85,"language":86,"meta":87,"style":87},"language-python shiki shiki-themes github-light github-dark","from typing import Iterator, Dict, Any\nimport csv\n\ndef stream_rows_from_source(filepath: str) -> Iterator[Dict[str, Any]]:\n \"\"\"Yield dictionaries row-by-row to prevent OOM on multi-million datasets.\"\"\"\n with open(filepath, \"r\", encoding=\"utf-8\") as f:\n reader = csv.DictReader(f)\n for row in reader:\n yield dict(row) # Ensure mutable dict copy if needed\n","python","",[15,89,90,109,117,124,149,156,193,204,219],{"__ignoreMap":87},[91,92,95,99,103,106],"span",{"class":93,"line":94},"line",1,[91,96,98],{"class":97},"szBVR","from",[91,100,102],{"class":101},"sVt8B"," typing ",[91,104,105],{"class":97},"import",[91,107,108],{"class":101}," Iterator, Dict, Any\n",[91,110,112,114],{"class":93,"line":111},2,[91,113,105],{"class":97},[91,115,116],{"class":101}," csv\n",[91,118,120],{"class":93,"line":119},3,[91,121,123],{"emptyLinePlaceholder":122},true,"\n",[91,125,127,130,134,137,141,144,146],{"class":93,"line":126},4,[91,128,129],{"class":97},"def",[91,131,133],{"class":132},"sScJk"," stream_rows_from_source",[91,135,136],{"class":101},"(filepath: ",[91,138,140],{"class":139},"sj4cs","str",[91,142,143],{"class":101},") -> Iterator[Dict[",[91,145,140],{"class":139},[91,147,148],{"class":101},", Any]]:\n",[91,150,152],{"class":93,"line":151},5,[91,153,155],{"class":154},"sZZnC"," \"\"\"Yield dictionaries row-by-row to prevent OOM on multi-million datasets.\"\"\"\n",[91,157,159,162,165,168,171,174,178,181,184,187,190],{"class":93,"line":158},6,[91,160,161],{"class":97}," with",[91,163,164],{"class":139}," open",[91,166,167],{"class":101},"(filepath, ",[91,169,170],{"class":154},"\"r\"",[91,172,173],{"class":101},", ",[91,175,177],{"class":176},"s4XuR","encoding",[91,179,180],{"class":97},"=",[91,182,183],{"class":154},"\"utf-8\"",[91,185,186],{"class":101},") ",[91,188,189],{"class":97},"as",[91,191,192],{"class":101}," f:\n",[91,194,196,199,201],{"class":93,"line":195},7,[91,197,198],{"class":101}," reader ",[91,200,180],{"class":97},[91,202,203],{"class":101}," csv.DictReader(f)\n",[91,205,207,210,213,216],{"class":93,"line":206},8,[91,208,209],{"class":97}," for",[91,211,212],{"class":101}," row ",[91,214,215],{"class":97},"in",[91,217,218],{"class":101}," reader:\n",[91,220,222,225,228,231],{"class":93,"line":221},9,[91,223,224],{"class":97}," yield",[91,226,227],{"class":139}," dict",[91,229,230],{"class":101},"(row) ",[91,232,234],{"class":233},"sJ8bj","# Ensure mutable dict copy if needed\n",[77,236,238],{"id":237},"async-core-execute-with-generator-chunking","Async Core Execute with Generator Chunking",[82,240,242],{"className":84,"code":241,"language":86,"meta":87,"style":87},"import asyncio\nfrom itertools import islice\nfrom typing import Iterator, Dict, Any\nfrom sqlalchemy.ext.asyncio import AsyncEngine\nfrom sqlalchemy import Table, insert\n\nasync def execute_chunked_inserts(\n engine: AsyncEngine,\n table: Table,\n data_stream: Iterator[Dict[str, Any]],\n chunk_size: int = 25000\n) -> None:\n \"\"\"Execute bulk inserts using async connection and generator slicing.\"\"\"\n stmt = insert(table)\n chunk = list(islice(data_stream, chunk_size))\n\n async with engine.connect() as conn:\n try:\n while chunk:\n # SQLAlchemy 2.0 auto-detects sequences and uses executemany\n await conn.execute(stmt, chunk)\n chunk = list(islice(data_stream, chunk_size))\n await conn.commit()\n except Exception:\n await conn.rollback()\n raise\n",[15,243,244,251,263,273,285,297,301,315,320,325,336,351,363,369,380,394,399,415,423,432,438,447,458,466,477,485],{"__ignoreMap":87},[91,245,246,248],{"class":93,"line":94},[91,247,105],{"class":97},[91,249,250],{"class":101}," asyncio\n",[91,252,253,255,258,260],{"class":93,"line":111},[91,254,98],{"class":97},[91,256,257],{"class":101}," itertools ",[91,259,105],{"class":97},[91,261,262],{"class":101}," islice\n",[91,264,265,267,269,271],{"class":93,"line":119},[91,266,98],{"class":97},[91,268,102],{"class":101},[91,270,105],{"class":97},[91,272,108],{"class":101},[91,274,275,277,280,282],{"class":93,"line":126},[91,276,98],{"class":97},[91,278,279],{"class":101}," sqlalchemy.ext.asyncio ",[91,281,105],{"class":97},[91,283,284],{"class":101}," AsyncEngine\n",[91,286,287,289,292,294],{"class":93,"line":151},[91,288,98],{"class":97},[91,290,291],{"class":101}," sqlalchemy ",[91,293,105],{"class":97},[91,295,296],{"class":101}," Table, insert\n",[91,298,299],{"class":93,"line":158},[91,300,123],{"emptyLinePlaceholder":122},[91,302,303,306,309,312],{"class":93,"line":195},[91,304,305],{"class":97},"async",[91,307,308],{"class":97}," def",[91,310,311],{"class":132}," execute_chunked_inserts",[91,313,314],{"class":101},"(\n",[91,316,317],{"class":93,"line":206},[91,318,319],{"class":101}," engine: AsyncEngine,\n",[91,321,322],{"class":93,"line":221},[91,323,324],{"class":101}," table: Table,\n",[91,326,328,331,333],{"class":93,"line":327},10,[91,329,330],{"class":101}," data_stream: Iterator[Dict[",[91,332,140],{"class":139},[91,334,335],{"class":101},", Any]],\n",[91,337,339,342,345,348],{"class":93,"line":338},11,[91,340,341],{"class":101}," chunk_size: ",[91,343,344],{"class":139},"int",[91,346,347],{"class":97}," =",[91,349,350],{"class":139}," 25000\n",[91,352,354,357,360],{"class":93,"line":353},12,[91,355,356],{"class":101},") -> ",[91,358,359],{"class":139},"None",[91,361,362],{"class":101},":\n",[91,364,366],{"class":93,"line":365},13,[91,367,368],{"class":154}," \"\"\"Execute bulk inserts using async connection and generator slicing.\"\"\"\n",[91,370,372,375,377],{"class":93,"line":371},14,[91,373,374],{"class":101}," stmt ",[91,376,180],{"class":97},[91,378,379],{"class":101}," insert(table)\n",[91,381,383,386,388,391],{"class":93,"line":382},15,[91,384,385],{"class":101}," chunk ",[91,387,180],{"class":97},[91,389,390],{"class":139}," list",[91,392,393],{"class":101},"(islice(data_stream, chunk_size))\n",[91,395,397],{"class":93,"line":396},16,[91,398,123],{"emptyLinePlaceholder":122},[91,400,402,405,407,410,412],{"class":93,"line":401},17,[91,403,404],{"class":97}," async",[91,406,161],{"class":97},[91,408,409],{"class":101}," engine.connect() ",[91,411,189],{"class":97},[91,413,414],{"class":101}," conn:\n",[91,416,418,421],{"class":93,"line":417},18,[91,419,420],{"class":97}," try",[91,422,362],{"class":101},[91,424,426,429],{"class":93,"line":425},19,[91,427,428],{"class":97}," while",[91,430,431],{"class":101}," chunk:\n",[91,433,435],{"class":93,"line":434},20,[91,436,437],{"class":233}," # SQLAlchemy 2.0 auto-detects sequences and uses executemany\n",[91,439,441,444],{"class":93,"line":440},21,[91,442,443],{"class":97}," await",[91,445,446],{"class":101}," conn.execute(stmt, chunk)\n",[91,448,450,452,454,456],{"class":93,"line":449},22,[91,451,385],{"class":101},[91,453,180],{"class":97},[91,455,390],{"class":139},[91,457,393],{"class":101},[91,459,461,463],{"class":93,"line":460},23,[91,462,443],{"class":97},[91,464,465],{"class":101}," conn.commit()\n",[91,467,469,472,475],{"class":93,"line":468},24,[91,470,471],{"class":97}," except",[91,473,474],{"class":139}," Exception",[91,476,362],{"class":101},[91,478,480,482],{"class":93,"line":479},25,[91,481,443],{"class":97},[91,483,484],{"class":101}," conn.rollback()\n",[91,486,488],{"class":93,"line":487},26,[91,489,490],{"class":97}," raise\n",[53,492,494],{"id":493},"niche-optimization-driver-tuning-connection-pooling","Niche Optimization: Driver Tuning & Connection Pooling",[19,496,497,498,501,502,505,506,509,510,513,514,517,518,522],{},"Driver-level configuration drastically impacts throughput and memory stability during massive async inserts. For ",[15,499,500],{},"asyncpg",", disable the prepared statement cache to prevent unbounded memory growth during high-volume parameter binding. For ",[15,503,504],{},"psycopg",", enable native multi-row ",[15,507,508],{},"VALUES"," syntax generation to reduce network round-trips. Tune ",[15,511,512],{},"pool_size"," and ",[15,515,516],{},"max_overflow"," to accommodate concurrent worker processes without exhausting database connection limits. For deeper connection reuse strategies and transaction batching, reference ",[47,519,521],{"href":520},"\u002Fadvanced-query-patterns-and-bulk-data-operations\u002Fhigh-performance-bulk-inserts-and-updates\u002F","High-Performance Bulk Inserts and Updates",".",[77,524,526],{"id":525},"driver-specific-execution-options","Driver-Specific Execution Options",[82,528,530],{"className":84,"code":529,"language":86,"meta":87,"style":87},"from sqlalchemy.ext.asyncio import create_async_engine\n\n# asyncpg: Bypass cache memory leaks during massive inserts\nasyncpg_engine = create_async_engine(\n \"postgresql+asyncpg:\u002F\u002Fuser:pass@host\u002Fdb\",\n prepared_statement_cache_size=0,\n pool_size=20,\n max_overflow=10,\n execution_options={\"no_returning\": True}\n)\n\n# psycopg: Native multi-row INSERT syntax generation\npsycopg_engine = create_async_engine(\n \"postgresql+psycopg:\u002F\u002Fuser:pass@host\u002Fdb\",\n executemany_mode=\"values\",\n pool_size=20,\n max_overflow=10,\n execution_options={\"no_returning\": True}\n)\n",[15,531,532,543,547,552,562,570,582,594,606,628,633,637,642,651,658,670,680,690,706],{"__ignoreMap":87},[91,533,534,536,538,540],{"class":93,"line":94},[91,535,98],{"class":97},[91,537,279],{"class":101},[91,539,105],{"class":97},[91,541,542],{"class":101}," create_async_engine\n",[91,544,545],{"class":93,"line":111},[91,546,123],{"emptyLinePlaceholder":122},[91,548,549],{"class":93,"line":119},[91,550,551],{"class":233},"# asyncpg: Bypass cache memory leaks during massive inserts\n",[91,553,554,557,559],{"class":93,"line":126},[91,555,556],{"class":101},"asyncpg_engine ",[91,558,180],{"class":97},[91,560,561],{"class":101}," create_async_engine(\n",[91,563,564,567],{"class":93,"line":151},[91,565,566],{"class":154}," \"postgresql+asyncpg:\u002F\u002Fuser:pass@host\u002Fdb\"",[91,568,569],{"class":101},",\n",[91,571,572,575,577,580],{"class":93,"line":158},[91,573,574],{"class":176}," prepared_statement_cache_size",[91,576,180],{"class":97},[91,578,579],{"class":139},"0",[91,581,569],{"class":101},[91,583,584,587,589,592],{"class":93,"line":195},[91,585,586],{"class":176}," pool_size",[91,588,180],{"class":97},[91,590,591],{"class":139},"20",[91,593,569],{"class":101},[91,595,596,599,601,604],{"class":93,"line":206},[91,597,598],{"class":176}," max_overflow",[91,600,180],{"class":97},[91,602,603],{"class":139},"10",[91,605,569],{"class":101},[91,607,608,611,613,616,619,622,625],{"class":93,"line":221},[91,609,610],{"class":176}," execution_options",[91,612,180],{"class":97},[91,614,615],{"class":101},"{",[91,617,618],{"class":154},"\"no_returning\"",[91,620,621],{"class":101},": ",[91,623,624],{"class":139},"True",[91,626,627],{"class":101},"}\n",[91,629,630],{"class":93,"line":327},[91,631,632],{"class":101},")\n",[91,634,635],{"class":93,"line":338},[91,636,123],{"emptyLinePlaceholder":122},[91,638,639],{"class":93,"line":353},[91,640,641],{"class":233},"# psycopg: Native multi-row INSERT syntax generation\n",[91,643,644,647,649],{"class":93,"line":365},[91,645,646],{"class":101},"psycopg_engine ",[91,648,180],{"class":97},[91,650,561],{"class":101},[91,652,653,656],{"class":93,"line":371},[91,654,655],{"class":154}," \"postgresql+psycopg:\u002F\u002Fuser:pass@host\u002Fdb\"",[91,657,569],{"class":101},[91,659,660,663,665,668],{"class":93,"line":382},[91,661,662],{"class":176}," executemany_mode",[91,664,180],{"class":97},[91,666,667],{"class":154},"\"values\"",[91,669,569],{"class":101},[91,671,672,674,676,678],{"class":93,"line":396},[91,673,586],{"class":176},[91,675,180],{"class":97},[91,677,591],{"class":139},[91,679,569],{"class":101},[91,681,682,684,686,688],{"class":93,"line":401},[91,683,598],{"class":176},[91,685,180],{"class":97},[91,687,603],{"class":139},[91,689,569],{"class":101},[91,691,692,694,696,698,700,702,704],{"class":93,"line":417},[91,693,610],{"class":176},[91,695,180],{"class":97},[91,697,615],{"class":101},[91,699,618],{"class":154},[91,701,621],{"class":101},[91,703,624],{"class":139},[91,705,627],{"class":101},[91,707,708],{"class":93,"line":425},[91,709,632],{"class":101},[53,711,713],{"id":712},"error-resolution-common-async-bulk-insert-failures","Error Resolution: Common Async & Bulk Insert Failures",[715,716,717,733],"table",{},[718,719,720],"thead",{},[721,722,723,727,730],"tr",{},[724,725,726],"th",{},"Error",[724,728,729],{},"Root Cause",[724,731,732],{},"Production Fix",[734,735,736,756,768,783],"tbody",{},[721,737,738,744,747],{},[739,740,741],"td",{},[15,742,743],{},"InterfaceError: connection already closed",[739,745,746],{},"Transaction scope exits before commit.",[739,748,749,750,752,753,522],{},"Ensure ",[15,751,44],{}," executes explicitly before ",[15,754,755],{},"__aexit__",[721,757,758,762,765],{},[739,759,760],{},[15,761,62],{},[739,763,764],{},"Loading full dataset into Python memory.",[739,766,767],{},"Cap chunk size at 10k–50k and stream data via generators.",[721,769,770,775,778],{},[739,771,772],{},[15,773,774],{},"StatementError: (psycopg2.errors.UndefinedTable)",[739,776,777],{},"Schema mismatch or sync fallback in async loop.",[739,779,780,781,522],{},"Verify table metadata is reflected\u002Fcreated and dialect is initialized with ",[15,782,28],{},[721,784,785,790,793],{},[739,786,787],{},[15,788,789],{},"asyncpg.exceptions.TooManyConnectionsError",[739,791,792],{},"Connection pool exhaustion under concurrent load.",[739,794,795,796,799],{},"Adjust ",[15,797,798],{},"create_async_engine(pool_size=20, max_overflow=10)"," and implement backoff retries.",[53,801,803],{"id":802},"critical-pitfalls-to-avoid","Critical Pitfalls to Avoid",[805,806,807,834,843,849,863],"ul",{},[808,809,810,818,819,822,823,826,827,830,831,522],"li",{},[811,812,813,814,817],"strong",{},"Synchronous ",[15,815,816],{},"execute()"," in async loops:"," Calling ",[15,820,821],{},"conn.execute()"," without ",[15,824,825],{},"await"," or using synchronous engines inside ",[15,828,829],{},"asyncio"," triggers ",[15,832,833],{},"RuntimeError: cannot run in event loop",[808,835,836,842],{},[811,837,838,839,841],{},"Omitted ",[15,840,44],{},":"," SQLAlchemy 2.0 does not auto-commit on context exit. Missing explicit commits cause silent transaction rollbacks.",[808,844,845,848],{},[811,846,847],{},"Single-list parameter dumping:"," Passing millions of rows as one sequence exceeds DBAPI parameter limits (e.g., PostgreSQL's 65,535 cap). Always chunk.",[808,850,851,858,859,862],{},[811,852,853,854,857],{},"Enabling ",[15,855,856],{},"echo=True"," in production:"," Serializes millions of parameter bindings to ",[15,860,861],{},"stdout",", causing I\u002FO bottlenecks and process crashes.",[808,864,865,871],{},[811,866,867,868,841],{},"Neglecting ",[15,869,870],{},"no_returning=True"," Forces SQLAlchemy to fetch generated IDs for every row, exhausting application RAM and doubling network latency.",[53,873,875],{"id":874},"faq","FAQ",[19,877,878,884,885,888],{},[811,879,880,881,883],{},"Does SQLAlchemy 2.0 ",[15,882,17],{}," automatically batch rows?","\nYes. Passing a sequence of dictionaries triggers ",[15,886,887],{},"executemany=True"," implicitly. SQLAlchemy delegates batch execution to the underlying DBAPI driver's optimized bulk path.",[19,890,891,894],{},[811,892,893],{},"What is the optimal chunk size for millions of rows?","\nBetween 10,000 and 50,000 rows per chunk. This range balances network round-trip overhead, transaction log growth, and Python memory allocation without hitting driver parameter limits.",[19,896,897,900,901,904,905,908],{},[811,898,899],{},"How do I handle auto-incrementing IDs during async bulk inserts?","\nUse ",[15,902,903],{},"execution_options={\"no_returning\": True}"," to skip fetching generated IDs, or rely on database-level sequences. Fetching millions of ",[15,906,907],{},"RETURNING"," values negates bulk performance gains.",[19,910,911,921,922,925,926,929,930,933],{},[811,912,913,914,917,918,920],{},"Can I use ",[15,915,916],{},"INSERT ... ON CONFLICT"," with async ",[15,919,17],{},"?","\nYes. Construct the statement using ",[15,923,924],{},"insert(table).on_conflict_do_nothing()"," or ",[15,927,928],{},"on_conflict_do_update()",", then pass the compiled statement to ",[15,931,932],{},"await conn.execute()"," with chunked dictionaries.",[935,936,937],"style",{},"html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .s4XuR, html code.shiki .s4XuR{--shiki-default:#E36209;--shiki-dark:#FFAB70}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":87,"searchDepth":111,"depth":111,"links":939},[940,944,947,948,949],{"id":55,"depth":111,"text":56,"children":941},[942,943],{"id":79,"depth":119,"text":80},{"id":237,"depth":119,"text":238},{"id":493,"depth":111,"text":494,"children":945},[946],{"id":525,"depth":119,"text":526},{"id":712,"depth":111,"text":713},{"id":802,"depth":111,"text":803},{"id":874,"depth":111,"text":875},"To batch insert millions of rows using SQLAlchemy 2.0 Core, initialize an AsyncEngine with create_async_engine(), wrap operations in an async with engine.connect() transaction block, and pass chunked dictionaries directly to await conn.execute(stmt, chunk). SQLAlchemy 2.0 automatically detects sequences of dictionaries and delegates execution to the underlying DBAPI's executemany implementation. Always call await conn.commit() explicitly before the context manager exits to prevent silent rollbacks. This execution model aligns with established Advanced Query Patterns and Bulk Data Operations for scalable, production-grade data pipelines.","md",{},"\u002Fadvanced-query-patterns-and-bulk-data-operations\u002Fhigh-performance-bulk-inserts-and-updates\u002Fbatch-inserting-millions-of-rows-with-sqlalchemy-coreexecute",{"title":5,"description":950},"advanced-query-patterns-and-bulk-data-operations\u002Fhigh-performance-bulk-inserts-and-updates\u002Fbatch-inserting-millions-of-rows-with-sqlalchemy-coreexecute\u002Findex","Hl_P_zJiDbzT8Fq-0cvf56MDemPLVQfzOOmjMKaIuXE",1778149144398]