Sketches　are　useful　for　network　measurement　thanks　to　their　low　resource　overheads　and　theoretically　bounded　accuracy.　However,　their　network-wide　deployment　suffers　from　the　trade-off　between　optimality　and　scalability:　(1)　Most　solutions　rely　on　mixed　integer　linear　programming　(MILP)　solvers　to　provide　the　optimal　decisions.　But　they　are　time-consuming　and　can　hardly　scale　to　large-scale　deployment　scenarios.　(2)　While　heuristics　achieve　scalability,　they　deteriorate　resource　and　performance　overheads.　We　propose　Eagle,　a　framework　that　achieves　scalable　and　near-optimal　network-wide　sketch　deployment.　Our　key　idea　is　to　decompose　network-wide　sketch　deployment　into　sub-problems.　Such　decomposition　allows　Eagle　to　(1)　simultaneously　optimize　switch　resource　consumption　and　end-to-end　performance　(retaining　optimality),　and　(2)　incorporate　time-saving　techniques　into　sub-problem　solving　(achieving　scalability).　Compared　to　existing　solutions,　Eagle　improves　scalability　by　up　to　255×　with　negligible　loss　of　optimality.　It　has　also　saved　administrators　in　a　production　network　days　of　efforts　and　reduced　the　operation　time　from　O(hour)　to　O(second).　　©　2024　Copyright　is　held　by　the　owner/author(s).　Publication　rights　licensed　to　ACM.