Network　telemetry　is　an　essential　part　of　network　management　and　infrastructure.　Among　them,　cardinality　telemetry　provides　statistics　on　network　connectivity　and　distribution.　Network-wide　cardinality　telemetry　refers　to　the　deployment　of　multiple　telemetry　nodes　in　network　for　cardinality　estimate.　This　requires　the　deployed　data　structure　to　be　mergeable,　enabling　the　consolidation　of　data　from　different　nodes.　Unfortunately,　existing　mergeable　data　structures　can＇t　simultaneously　address　two　important　criterions　of　cardinality　telemetry:　measurement　accuracy　and　estimation　interval.　We　propose　CardSketch,　aiming　to　adjust　attention　to　cardinality　telemetry　based　on　changes　of　the　network　state.　CardSketch　incorporates　a　shift　attention　mechanism　that　leverages　the　randomness　of　hash　functions　to　achieve　unbiased　transformations　between　data　structures.　This　mechanism　enables　real-time　selection　of　cardinality　estimation　methods　based　on　the　network＇s　state　while　preserving　the　original　telemetry　information　as　much　as　possible　during　the　attention　shift.　We　have　implemented　prototypes　of　CardSketch　in　software　and　hardware.　Through　extensive　experimentation,　the　results　demonstrate　that　CardSketch　achieves　excellent　cardinality　telemetry　with　minimal　memory　overhead.　Even　with　a　mere　50KB　of　memory　space,　it　achieves　a　measurement　precision　of　87.75%　and　a　measurement　recall　of　91.49%.　Additionally,　CardSketch　supports　multi-point　aggregation　and　arbitrary　partial　key　queries.